@simplifyinAI: A handful of well-known 3D human research projects, HumanNeRF, Neural Body, KeypointNeRF, all lean on the same open-sou…
Summary
EasyMocap is an open-source toolbox built by Zhejiang University that enables 3D human pose estimation from regular RGB videos, used by research projects like HumanNeRF, but with licensing considerations due to SMPL body models.
View Cached Full Text
Cached at: 08/27/26, 09:55 AM
A handful of well-known 3D human research projects, HumanNeRF, Neural Body, KeypointNeRF, all lean on the same open-source toolbox underneath.
It’s called EasyMocap, built by Zhejiang University’s 3D vision lab.
before: traditional motion capture needs marker suits, specialized rigs, and a dedicated studio.
now: EasyMocap estimates full 3D body, hand, and face pose straight from regular RGB video, even footage pulled from YouTube, fitted to the same body models researchers already use.
Worth knowing: last major release was 2021, and licensing isn’t shown as a standard open badge.
It also builds on SMPL body models, which usually carry their own non-commercial academic license, worth checking both before using this beyond research.
Similar Articles
SAM 3D Body: Robust Full-Body Human Mesh Recovery
SAM 3D Body is a promptable 3D human mesh recovery model using a novel parametric representation (MHR) and encoder-decoder architecture, achieving state-of-the-art performance with strong generalization. The model supports auxiliary prompts and is open-source.
@axichuhai: This free and open-source 3D motion capture tool, freemocap, has garnered 9K stars on GitHub. No professional capture equipment needed, just a few ordinary cameras. It transforms multi-view geometry problems into computer vision tasks, using spatial calibration algorithms + deep learning models to extract precise 3D human skeleton data from 2D footage of multiple ordinary cameras…
Freemocap is a free and open-source 3D motion capture tool. It requires only ordinary cameras to reconstruct precise 3D human skeleton data using spatial calibration and deep learning models, supporting multiple export formats.
@_akhaliq: LingBot-Video is out on Hugging Face MoE-based video foundation model built for embodied intelligence 30B params, only …
LingBot-Video, a 30B parameter MoE-based video foundation model for embodied intelligence, has been released on Hugging Face with only 3B active parameters at inference, augmented with 70K hours of embodied data.
HumanNet: Scaling Human-centric Video Learning to One Million Hours
HumanNet is a large-scale human-centric video dataset with one million hours of annotated footage, designed to train vision-language-action models. It demonstrates that egocentric human video can effectively replace robot data for embodied intelligence tasks.
robbyant/lingbot-video-moe-30b-a3b
LingBot-Video is the first open-source large-scale MoE video generation model for embodied intelligence, featuring efficient MoE architecture, massive embodied data training, and multi-reward system for high aesthetics, physical rationality, and task completion.