camera-comfyUI

camera-comfyUI

Custom ComfyUI nodes for advanced reprojections, point cloud processing, and camera-driven workflows.

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Table of Contents

• Overview
• Features
• Installation
• Node Categories
• Node Reference
• Video → 4D World
• Workflows
• Example Workflows
• Contributing
• TODO List

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Overview

A collection of ComfyUI custom nodes to handle diverse camera projections (pinhole, fisheye, equirectangular), depth‐to‐point cloud conversions, dynamic reprojections, and inpainting/outpainting pipelines. Use these nodes to craft complex VR and 3D‐aware image transformations with minimal setup.

Features

• ⚡ Continuous Reprojection: Transform images and depth maps between projection models.
• 🌐 Point Cloud Pipelines: Convert depth to 3D, clean, transform, and reproject point clouds.
• 🎥 Camera Motion & Outpainting: Animate camera trajectories, perform text‐guided outpainting in arbitrary views.
• 📦 Modular Nodes: Groupable ComfyUI nodes for flexible graph composition.
• 🛠️ Example Workflows: Ready‐to‐use JSON workflows demonstrating reprojection, inpainting, and view synthesis.

Installation

Option A — ComfyUI Manager (recommended)

The node pack is published to the ComfyUI Registry as camera-comfyui (publisher alexk). In ComfyUI, open Manager → Custom Nodes Manager, search for camera-comfyUI, and click Install, then restart ComfyUI.

Installation is fully automatic: ComfyUI-Manager installs requirements.txt and then runs this pack's install.py, which sets up everything the optional nodes need — no manual steps:

• vggt (VideoPoseEstimator) — pip-installed from GitHub over https (it is not on PyPI).
• SHARP (ImageToSplat, VideoToFusedSplats, …) — the submodules/ml-sharpt checkout is bundled in the registry package (and fetched via git submodule/clone for git installs), and its Python deps come from requirements.txt.
• gsplat — pip-installed; its CUDA kernels JIT-compile on first use.
• ComfyUI-Flux-Inpainting (OutpaintAnyProjection, SplatTrajectoryEnricher) — cloned automatically into custom_nodes/inpainting_flux (skipped if you already have the pack under any of its usual folder names).
• Example inputs — the sample image/trajectory files referenced by the bundled workflows are copied into ComfyUI's input/ folder, so the templates run immediately.

Each step is optional and non-fatal: if one fails (e.g. no network), only the nodes that need it stay disabled — re-run python install.py inside the pack folder to retry.

Maintainers: releases are automated — bumping version in pyproject.toml on main triggers .github/workflows/publish_action.yml, which publishes the new version to the registry (requires the REGISTRY_ACCESS_TOKEN repo secret).

Option B — Manual install (git)

1. Clone into your ComfyUI custom nodes folder:

   git clone https://github.com/Alexankharin/camera-comfyUI.git custom_nodes/camera-comfyUI
   

2. System Dependencies (Ubuntu):

   sudo apt-get update && sudo apt-get install build-essential ffmpeg libsm6 libxext6 -y
   

3. Python Requirements + optional dependencies — one command sets up everything (base requirements, vggt, the SHARP submodule, gsplat, and the inpainting_flux sibling pack):

   cd custom_nodes/camera-comfyUI && python install.py
   

   This is the same script ComfyUI-Manager runs automatically; it is idempotent, and every optional step is non-fatal.

   What it covers (for reference — no manual action needed):

   • gsplat — CUDA-accelerated Gaussian splat rasterizer. Required by SplatPolish, SHARP, and the fast render backend for RenderSplat / RenderSplats4D*. Kernels JIT-compile on first use (needs a CUDA GPU + matching PyTorch build).
   • vggt — camera pose + depth estimation (VideoPoseEstimator). Not on PyPI — installed with pip install git+https://github.com/facebookresearch/vggt.git. A sibling clone of facebookresearch/vggt in your ComfyUI root also works. The facebook/VGGT-1B weights (~5 GB) download via huggingface_hub on first use.
   • CoTracker3 — point tracking for EstimateTracks. Fetched automatically via torch.hub on first use.
   • SHARP — image→splat prediction (ImageToSplat, FisheyeToGaussian, VideoToFusedSplats, SplatTrajectoryEnricher). Lives as the git submodule at submodules/ml-sharpt (apple/ml-sharp); install.py initializes it for you.
   • ComfyUI-Flux-Inpainting — cloned into custom_nodes/inpainting_flux if missing. Any of the usual folder names (inpainting_flux, ComfyUI-Flux-Inpainting, ComfyUI-Flux-Inpainting-main) is detected — no renaming needed.

4. Additional Nodes (only for some example workflows):

   • ComfyUI-Image-Filters — install via Manager or clone into custom_nodes.

5. Flux Models (Hugging Face, only for gated models):

   huggingface-cli login
   

6. Restart ComfyUI to load new nodes.

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Node Categories

• Reprojection Nodes

  • ReprojectImage, ReprojectDepth, OutpaintAnyProjection

• Matrix Nodes

  • TransformToMatrix, TransformToMatrixManual

• Depth Nodes

  • DepthEstimatorNode, DepthToImageNode, ZDepthToRayDepthNode
  • CombineDepthsNode, DepthRenormalizer, FisheyeDepthEstimator

• Point Cloud Nodes

  • DepthToPointCloud, TransformPointCloud, ProjectPointCloud, PointCloudUnion
  • PointCloudCleaner, LoadPointCloud, SavePointCloud, ProjectAndClean, DepthEdgeFilter

• Trajectory Nodes

  • CameraMotionNode, CameraInterpolationNode, CameraTrajectoryNode
  • SaveTrajectory, LoadTrajectory, PointcloudTrajectoryEnricher

• Gaussian Splat Nodes

  • LoadPlySplat, SavePlySplat, ImageToSplat, FisheyeToGaussian
  • RotateSplats, MergeSplats, FuseSplats, RenderSplat
  • VideoToFusedSplats, SplatPolish

• 4D Gaussian Splat Nodes

  • MotionMaskFromDepth, EstimateTracks, TracksToTrajectories, SplitSplatsByMask
  • BuildSplats4D, RenderSplats4DFrame, RenderSplats4DVideo
  • SaveSplats4D, LoadSplats4D

• Pose Nodes

  • VideoPoseEstimator, TrajectoryInvert, TrajectoryCompose

• World Nodes

  • DepthScaleAnchor, SplatTrajectoryEnricher, SphereSplatSeed

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Node Reference

(See inline tooltips in ComfyUI for parameter details.)

| Node | Description | | ------------------------- | ----------------------------------------------------------------------------- | | ReprojectImage | Reproject image between projection types (Pinhole, Fisheye, Equirectangular). | | ReprojectDepth | Same as above but for depth maps. | | OutpaintAnyProjection | Extracts a patch in any view, outpaints (Flux), reprojects back. | | DepthEstimatorNode | Runs HF Depth‐Anything-v2 models to produce metric depth. | | DepthToPointCloud | Converts Depth and image to → 3D point cloud tensor (N×7). | | DepthToImageNode | Converts depth to image (N×3) using a color map. | | ZDepthToRayDepthNode | Converts Z-depth (output of metric-depth-anything) to ray depth to compensate lens curvature. | | TransformPointCloud | Applies 4×4 rotation matrix to point cloud. | | ProjectPointCloud | Z-buffer–based projection of point cloud into image + mask. | | PointCloudCleaner | Removes isolated points via voxel filtering. | | PointCloudUnion | Combines multiple point clouds into one. | | LoadPointCloud | Loads a point cloud from .npy or .ply format. | | SavePointCloud | Saves a point cloud to .npy or .ply format. | | CameraMotionNode | Generates image and mask sequences along a camera trajectory with optional mask dilation/inversion. | | CameraInterpolationNode | Builds a trajectory tensor from two poses. | | CameraTrajectoryNode | Interactive Open3D GUI for recording camera waypoints. | | SaveTrajectory | Saves a trajectory tensor to a file. | | LoadTrajectory | Loads a trajectory tensor from a file. | | VideoCameraMotionSequence | Processes video frames and depth maps along a camera trajectory, generating reprojected outputs. | | DepthFramesToVideo | Converts a sequence of depth maps into video frame tensors for saving. | | VideoMetricDepthEstimate | Estimates metric depth for a sequence of frames using VideoDepthAnything. | | DepthEdgeFilter | Detects "flying pixel" depth discontinuities and outputs a validity mask (1.0 = valid). | | LoadPlySplat | Loads a 3D Gaussian Splatting .ply file into a GSPLAT object. | | SavePlySplat | Saves a GSPLAT to the ComfyUI output directory as a .ply file. | | ImageToSplat | Predicts Gaussian splats from a single image using SHARP. | | FisheyeToGaussian | Reprojects a fisheye view to multiple pinhole angles, predicts splats, rotates and merges them. | | RotateSplats | Applies a 4×4 transform matrix to a splat cloud. | | MergeSplats | Concatenates two GSPLAT objects into one. | | FuseSplats | Fuses two splat clouds with weighted voxel merging (keep/discard/average/smart modes). | | RenderSplat | Renders a splat cloud from a camera pose into an image + mask. | | VideoToFusedSplats | Runs SHARP on video keyframes, scale-aligns to metric depth, filters dynamic pixels, and fuses all keyframes into one world-frame splat cloud. | | SplatPolish | Optimizes a world-frame splat cloud against posed video frames (L1 + D-SSIM) using gsplat's differentiable rasterizer. | | MotionMaskFromDepth | Detects dynamic pixels from a depth+pose sequence (1.0 = moving). | | EstimateTracks | Runs CoTracker3 on a video; returns tracks [T,N,2] (pixels) and visibility [T,N]. | | TracksToTrajectories | Unprojects 2D tracks with depth and camera poses into world-space 3D trajectories [T,M,3]. | | SplitSplatsByMask | Projects splat centers into a 2D mask and splits the cloud into inside/outside parts. | | BuildSplats4D | Builds a 4D splat scene: each canonical splat follows a kNN blend of track control-point motions. | | RenderSplats4DFrame | Evaluates the 4D scene at a single time value and renders it from a given camera. | | RenderSplats4DVideo | Interpolates the camera path, sweeps time from start to end, and renders each frame. | | SaveSplats4D | Saves a GSPLAT4D scene as an .npz archive (plus optional per-frame PLYs). | | LoadSplats4D | Loads a GSPLAT4D scene from an .npz archive. | | VideoPoseEstimator | VGGT-based per-frame camera poses [T,4,4], depth maps, FOV and depth confidence from a video clip. | | TrajectoryInvert | Inverts each 4×4 pose (world-to-camera ↔ camera-to-world). | | TrajectoryCompose | Per-frame matrix product A @ B; a single 4×4 input broadcasts over the other. | | DepthScaleAnchor | Robustly aligns a depth map to a reference depth via disparity-domain scale(+shift). | | SplatTrajectoryEnricher | Expands a splat world along a trajectory: render, outpaint holes with Flux, lift with SHARP, scale-align, smart-stitch. | | SphereSplatSeed | Converts an equirectangular panorama into a Gaussian sphere seeding a 360° world. |

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Video → 4D World

Turn a monocular video into a navigable 4D (3D + time) Gaussian splat scene and re-render it from any novel camera trajectory. The reference workflow is workflows/video_to_4d_world.json; the stages are:

1. Pose & depth (VGGT) — VideoPoseEstimator estimates per-frame world-to-camera poses [T,4,4], depth maps, FOV and depth confidence from the input frames. Since the depth maps are Z-depths, run ZDepthToRayDepthNode before any node that expects ray depth (see caveats below). DepthEdgeFilter can additionally mask out flying pixels at depth discontinuities.
2. Motion masking — MotionMaskFromDepth warps depth between frames using the estimated poses and flags pixels whose residual is too large as dynamic (moving objects vs. static background).
3. Static splat fusion + polish — VideoToFusedSplats runs SHARP on keyframes, keeps only static pixels (via the motion mask), scale-aligns each keyframe to metric depth, transforms splats into the world frame and fuses them incrementally. SplatPolish then fine-tunes the fused cloud photometrically against the posed video frames.
4. Tracked dynamic 4D Gaussians — EstimateTracks (CoTracker3) tracks a dense point grid across the video; TracksToTrajectories lifts the tracks to world-space 3D using depth + poses; SplitSplatsByMask separates dynamic splats from the static background; BuildSplats4D binds the dynamic canonical splats to track control points via kNN blending, producing a GSPLAT4D scene.
5. Render a novel trajectory — build any new camera path (e.g. CameraInterpolationNode, TrajectoryCompose to retarget relative to a source pose) and render with RenderSplats4DVideo (or single frames with RenderSplats4DFrame). Save/reload scenes with SaveSplats4D / LoadSplats4D.

Static-camera fisheye variant — for footage from a locked-off 180° fisheye camera, workflows/fisheye_static_video_to_4d.json skips pose estimation entirely (identity trajectory), uses the batched FisheyeDepthEstimator for per-frame radial depth and FisheyeToGaussian on frame 0 for the whole static world, then follows the same track → split → BuildSplats4D → render path (all 4D nodes accept the FISHEYE projection directly).

Caveats

• Z-depth vs ray depth: depth estimators (including VideoPoseEstimator) output Z-depth; point-cloud and splat lifting nodes expect ray depth. Insert ZDepthToRayDepthNode where needed, or geometry will bow at wide FOVs.
• SplatPolish requires gsplat + CUDA: without them it can fall back to the differentiable torch renderer at reduced resolution, which is extremely slow (minutes per 100 iterations).
• EstimateTracks downloads CoTracker3 via torch.hub on first use — expect a one-time download and allow network access.
• VideoPoseEstimator downloads facebook/VGGT-1B (~5 GB) on first use via huggingface_hub.

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Workflows

A set of JSON workflows illustrating typical use cases. Once the pack is installed they appear in ComfyUI under Workflow → Browse Templates (with thumbnails); the files live in workflows/ and can also be loaded directly. Each workflow contains an embedded “About this workflow” note in the canvas explaining its stages, what to set, and what it needs — and references the bundled example inputs that install.py copies into your ComfyUI input/ folder, so they run as-is on a fresh install.

Extras below means dependencies beyond this pack and Depth-Anything V2 (which auto-downloads); inpainting_flux is installed automatically by install.py.

| Workflow | Description | Extras | | --- | --- | --- | | demo_camera_workflow.json | Minimal demo: rotate the camera and reproject pinhole → equirectangular, with coverage mask | — | | Outpaint_node_test.json | One-patch smoke test of OutpaintAnyProjection | inpainting_flux | | Outpaint_fisheye180.json | Pinhole 90° → full 180° fisheye via five chained OutpaintAnyProjection passes + composite/upscale | inpainting_flux | | outpainting_fisheye_flux.json | Manual version of the above: explicit Flux Inpainting + reprojection stages | inpainting_flux, RealESRGAN | | fisheye_to_pointcloud.json | Fisheye 180° → metric depth → point cloud (.ply/.npy) | — | | PointCloud.json | Single image → point cloud → cleaned novel-view render | Image-Filters (optional) | | pointcloud_walker.json | Image → point cloud → camera fly-through WEBM | — | | test_pointcloud_loading.json | Reload a saved point cloud and orbit-render it | — | | record_trajectory.json | Record a camera trajectory .npy for LoadTrajectory (two poses → SE(3) interpolation → SaveTrajectory) | — | | pointcloud_inpaint.json | Enrich a cloud: Flux-inpaint disocclusions, lift them to 3D, merge, orbit render | inpainting_flux | | PC_enricher.json | One-node version of the above: PointcloudTrajectoryEnricher along a saved trajectory | inpainting_flux | | sbs180_workflow.json | Synthesize the second eye of a VR180 stereo pair from one fisheye view | inpainting_flux | | video_camera.json | Re-shoot a video with a new camera move; WAN VACE regenerates disocclusions, Florence2 auto-captions | VHS, Florence2; WAN 2.1 VACE + Video-Depth-Anything models | | wan_vace_ref_to_video.json | Still fisheye image + recorded trajectory → WAN VACE camera-move video | WAN 2.1 VACE models; VHS (optional MP4 export) | | video_to_4d_world.json | Video → 4D world: VGGT poses/depth → motion masking → fused static splats + polish → tracked dynamic 4D Gaussians → novel-trajectory render. | — | | video_to_4d_walkable_world.json | Video → 4D WALKABLE world (test-friendly defaults): polished static splats enriched along a walk trajectory (SplatTrajectoryEnricher, Flux outpaint + SHARP) → 4D scene → walk-through render + .ply/.npz exports for free walking in external 3DGS viewers. | inpainting_flux | | fisheye_static_video_to_4d.json | Static-camera 180° fisheye video → 4D Gaussian scene → novel-path render. No pose estimation needed: identity trajectory, batched fisheye depth, frame-0 splats split into static world + dynamic canonical. | VHS |

Superseded reference graphs live in workflows/legacy/ (kept out of the template browser): outpainting_fisheye.json (SD-inpaint-checkpoint variant of the flux outpaint) and Fisheye_depth_workflow.json (the multi-view depth fusion that FisheyeDepthEstimator now performs internally).

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Example Workflows

1. demo_camera_workflow.json

Basic reprojection pipeline: apply masks, rotate pinhole camera, outpaint fisheye, move point cloud, reproject.

<div style="display:flex; gap:10px;"> <img src="demo_images/initial.png" alt="Initial image" width="45%" /> <img src="demo_images/Pinhole_camera_rotation.png" alt="Pinhole Rotation" width="45%" /> </div>

2. legacy/outpainting_fisheye.json

Simplest text‐guided fisheye outpainting built with the core inpaint node (superseded by the Flux variant).

3. outpainting_fisheye_flux.json

Flux Inpainting ensures sharper results and explicit reprojection stages.

<div style="display:flex; gap:10px;"> <img src="demo_images/Fisheye_outpainted_flux_mask.png" alt="Flux Mask" width="60%" /> </div>

4. Outpaint_fisheye180.json

180° fisheye outpainting via the universal OutpaintAnyProjection node.

<img src="demo_images/Fisheye_outpainted_flux_dev.png" alt="Flux Dev" width="60%" />

5. legacy/Fisheye_depth_workflow.json

Convert fisheye images to metric depth and generate a PLY point cloud — the manual multi-view graph that FisheyeDepthEstimator now performs in one node (see fisheye_to_pointcloud.json).

<img src="demo_images/Depthmap.png" alt="Fisheye Depth→PointCloud" width="60%" />

6. Outpaint_node_test.json

<img src="demo_images/outpaint_any_proj.png" alt="Flux Dev" width="60%" />

Quick test for the universal outpaint node in arbitrary views and camera movement

7. PointCloud.json

Depth→PointCloud pipeline with interactive camera movement and reprojection views.

<img src="demo_images/Fisheye_camera_pointcloud_moved.png" alt="PointCloud Demo" width="60%" />

8. pointcloud_inpaint.json

Inpaint image with shifted camera and backproject for dynamic camera‐driven video outputs.

<img src="demo_images/Fisheye_camera_pointcloud_moved_outpainted.png" alt="PointCloud Inpaint" width="40%" /> <img src="demo_images/Camera_interpolation_pointcloud.gif" alt="PointCloud Inpaint Video" width="40%" />

9. sbs180_workflow.json

Take a wide-angle (fisheye or equirectangular) high-resolution (e.g., 4096×4096) image and generate a stereo pair by moving the camera horizontally. The output is a wide-angle stereo pair (side-by-side), simulating a fisheye or equirectangular stereo camera.

<img src="demo_images/equirect_stereo.gif" alt="Equirectangular Stereo Demo" width="80%" />

10. pointcloud_walker.json

Image → point cloud → camera fly-through rendered to WEBM (CameraTrajectoryNode + CameraMotionNode).

11. video_camera.json

This workflow demonstrates camera trajectory movement using the wan-vace video inpainting model. It generates smooth camera movements along a trajectory while filling missing regions with high-quality inpainting.

<div style="display:flex; gap:10px;"> <img src="demo_images/camera_movement.gif" alt="Camera Movement Demo" width="80%" /> </div>

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Trajectory Concept

A trajectory in camera-comfyUI is a sequence of camera poses, each represented as a 4×4 transformation matrix. This set of matrices defines the path and orientation of the camera through 3D space, enabling smooth and complex camera movements for view synthesis, point cloud rendering, and video generation.

Creating Trajectories

There are two main ways to create a trajectory:

• Camera Matrices Interpolation:
  Define two or more camera poses (as matrices), and interpolate between them to generate a smooth path. The CameraInterpolationNode automates this process, producing a trajectory tensor for use in camera motion nodes.

• Walking in Open3D Environment:
  Use the interactive Open3D GUI (CameraTrajectoryNode) to "walk" through the point cloud. As you move the camera, waypoints (poses) are recorded, forming a trajectory that can be exported and reused.

Using Trajectories

The CameraMotionNode takes a trajectory (set of matrices) and interpolates camera positions and orientations along it, producing smooth camera movements for rendering sequences or videos.

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Point Cloud Formats

Point clouds can be saved and loaded in two formats:

• .npy: Numpy array format (fast, preserves all tensor data, recommended for internal pipelines).
• .ply: Polygon File Format (widely supported, viewable in external 3D tools).

Use the SavePointCloud and LoadPointCloud nodes to handle I/O operations in either format.

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Contributing

Contributions welcome! Please open issues or PRs to add features, improve docs, or refine workflows.

TODO List

• [x] Add processing to pointcloud or depthmap to remove outlier and lonely points at depth borders.
• [x] Use built-in comfyUI mask type an image.
• [x] Unite nodes into groups to simplify workflows.
• [x] Create a single workflow for view synthesis (video_to_4d_world.json).
• [x] Implement easier and more flexible camera control - more complex camera movements with more than 2 points.
• [x] Add more examples and documentation for each node.
• [x] Add pointcloud union
• [x] Fix imports for renamed folders (e.g., inpainting_flux)
• [x] Integrate camera movement pipeline with video models (e.g., wan2.1) for smooth, high-quality inpainting along camera trajectories.
• [ ] Compressed export format for 4D scenes (current .npz stores raw tensors).
• [ ] SAM2-based refinement of motion masks (current masks come from depth-warp residuals only).
• [ ] Fisheye/equirectangular rendering through gsplat (e.g., via cubemap render + reprojection); the fast CUDA path is currently pinhole-only.