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$ show TencentARC/StereoCrafter

[account ↗] [repo ↗] APPLICATION ★ 525 75 ● active (last update 4 months ago)
reviewed by: nvidia/nemotron-3-super-120b-a12b

StereoCrafter is an application that converts monocular videos to stereoscopic 3D videos using a two-stage diffusion-based pipeline. The first stage estimates depth via DepthCrafter and generates a right-eye view through forward warping. The second stage refines the stereo video using a fine-tuned Stable Video Diffusion model with spatial tiling for high-resolution processing. The system relies on external dependencies including DepthCrafter and Forward-Warp submodules, and requires manual download of model weights. The codebase demonstrates modular design with clear separation between depth estimation, warping, and inpainting stages.

RATING7.5/10
QUALITY7.5/10
MAINTENANCEactive
DRIFTmedium
CONTRIBUTORS3
VELOCITY0.42
OPEN ISSUES29
CLOSED ISSUES13
LAST COMMIT4 months ago
torch (pip) ==2.0.1
torchvision (pip) ==0.15.2
fire (pip) ==0.5.0
accelerate (pip) ==1.0.1
safetensors (pip) ==0.4.3
transformers (pip) ==4.42.3
decord (pip) ==0.6.0
diffusers (pip) ==0.29.2
xformers (pip) ==0.0.20
av (pip) ==12.3.0
matplotlib (pip) ==3.7.5
mediapy (pip) ==1.2.2
opencv-python (pip) ==4.10.0.84
Files by Type (count)
Other (.jpg)
4
Python
3
Text
2
Git Ignore
1
Other (no ext)
1
Markdown
1
Other (.gif)
1
Shell
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Other (.mp4)
1
Files by Type (size)
Other (.gif)
30MB
Other (.mp4)
10MB
Other (.jpg)
2MB
Python
49KB
Markdown
6KB
Text
3KB
Shell
525B
Other (no ext)
235B
Git Ignore
42B

The LLM identified 2 files worth highlighting for reuse:

Defines a reusable StableVideoDiffusionInpaintingPipeline class that extends Diffusers for video inpainting with spatial tiling. Handles high-resolution videos by splitting frames into overlapping tiles, processing them independently, and blending results to avoid seams. Isolated from application-specific logic and adaptable to other video inpainting tasks.

file: pipelines/stereo_video_inpainting.py ↗ | tags: #diffusion-pipeline #video-inpainting #spatial-tiling

Contains blend_h, blend_v, and spatial_tiled_process functions that implement a generic spatial tiling strategy for processing large images/video frames in chunks. Includes horizontal/vertical blending to seamlessly recombine tiles. This pattern is reusable across various image/video processing tasks requiring fixed-input-size models.

file: inpainting_inference.py ↗ | tags: #image-tiling #blending #memory-efficient-processing
Last analyzed: 2026-08-01 02:59:24.708075
Next check: 2026-08-08 02:59:24.695313
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