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Diffuman4D

Diffuman4D is a Python research project that uses spatio-temporal diffusion models to synthesize consistent human-performance views and free-viewpoint 4D renderings from sparse-view videos.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Diffuman4D — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Diffuman4D when you want a research-code workflow for synthesizing additional views of a human performance from sparse camera input, or to explore its provided example and dataset-processing paths.

Who it is for

  • Researchers studying human-centric 3D and 4D view synthesis
  • Developers working with sparse-camera human-performance video
  • Practitioners exploring multi-view datasets and Gaussian Splatting reconstruction

Documented uses

  • Generate denser camera views from sparse-view human video.
  • Create 3D or 4D image grids from prepared example scenes.
  • Download and use re-annotated DNA-Rendering labels for research workflows.
Before you choose

Requirements & limits

  • The documented environment uses Python 3.12, Conda, repository requirements and EasyVolcap.
  • Inference expects prepared scene inputs including images, foreground masks, skeletons and camera data.
  • The README describes pretrained weights as automatically downloaded, with a manual Hugging Face download fallback.
  • The repository documents a nerfstudio-based 3D Gaussian Splatting stage; its 4D reconstruction alternative is described as forthcoming.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run the tiny 4D inference example

Generate a tiny 4D view grid from the repository's example scene.

Before you start

  • Python 3.12 and Conda
  • Diffuman4D repository and its requirements installed in an activated environment
  • EasyVolcap installed for the documented reconstruction and data-processing setup
  • Example test data containing images, foreground masks, skeletons and camera data
  • Pretrained weights, which inference attempts to download automatically

Steps

  1. Create and activate the diffuman4d Conda environment with Python 3.12.

  2. Install the repository requirements and EasyVolcap using the documented setup commands.

  3. Download the example data with scripts/download/download_dataset.py for krahets/diffuman4d_example, requesting images, fmasks, skeletons and cameras.

  4. Run the command below.

    Documented command
    python inference.py exp=demo_4d_tiny data.scene_label=0023_06 data.data_dir=./data/datasets--krahets--diffuman4d_example

Expected outcome

The documented example generates a tiny 4D image grid from four input cameras and 15 frames to 44 cameras and 15 frames; results are saved under `./output/results/dna_rendering/0023_06`.

Guide limitations

  • The README recommends the tiny 4D or 3D demo configuration for quicker testing on a single-GPU server.
  • If automatic model download encounters network issues, the README provides a manual pretrained-model download path.
  • This recipe covers inference, not reconstruction of a 4D Gaussian Splatting model.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
17 October 2026
Original website title
zju3dv/Diffuman4D · Official homepage title · retained in its source language
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