NVIDIA/vid2vid

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

8692 stars · 1207 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A PyTorch implementation of NVIDIA's video-to-video synthesis method for generating high-resolution (e.g., 2048x1024) photorealistic videos from semantic label maps, edge maps, or pose sequences. It is the official research code accompanying the NeurIPS 2018 paper 'Video-to-Video Synthesis'.

Use cases

• turning semantic segmentation maps into photorealistic street view videos
• synthesizing talking faces from facial edge maps
• generating human body motion videos from pose keypoints
• frame prediction for video synthesis
• reproducing the vid2vid NeurIPS 2018 paper results
• training video-to-video translation models on custom datasets

When to choose

• you need to translate semantic labels, edges, or poses into photorealistic video
• you want the reference implementation of the vid2vid paper for research
• you have an NVIDIA GPU and can work with PyTorch 0.4-era code

When to avoid

• you need a maintained production-ready video generation tool
• you want modern PyTorch versions or recent GPU support out of the box
• you need general-purpose video editing rather than conditional video synthesis

Facets

library · maturity maintenance

machine-learning deep-learning video-processing image-processing deep-learning computer-vision machine-learning python video-to-video-translation gan pytorch image-to-image-translation research-code neurips-2018 video linux macos gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="NVIDIA/vid2vid")