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SINC: Spatial Composition of 3D Human Motions for Simultaneous Action Generation Nikos Athanasiou · Mathis Petrovich · Michael J. Black · Gül Varol ICCV 2023 Official PyTorch implementation of the paper "SINC: Spatial Composition of 3D Human Motions for Simultaneous Action Generation"
This is a PyTorch deep learning framework and tool for human motion synthesis that generates 3D character animations from text prompts or action descriptions. It functions as a text-to-motion generator that converts natural language and categorical labels into temporally consistent 3D skeletal movement sequences. The system utilizes a transformer-based diffusion model to iteratively denoise motion data. It includes capabilities for action-conditioned generation, monocular-to-3D motion lifting, and motion sequence editing using text constraints. The framework incorporates geometric motion con
Official Pytorch implementation of the paper "MotionCLIP: Exposing Human Motion Generation to CLIP Space".
Official PyTorch implementation of the paper "TEMOS: Generating diverse human motions from textual descriptions", ECCV 2022 (Oral).
TEACH: Temporal Action Compositions for 3D Humans Nikos Athanasiou · Mathis Petrovich · Michael J. Black · Gül Varol 3DV 2022
The main features of athn-nik/teach are: Motion Generation.
Open-source alternatives to athn-nik/teach include: athn-nik/sinc — SINC: Spatial Composition of 3D Human Motions for Simultaneous Action Generation Nikos Athanasiou · Mathis Petrovich ·… guytevet/motion-diffusion-model — This is a PyTorch deep learning framework and tool for human motion synthesis that generates 3D character animations… guytevet/motionclip — Official Pytorch implementation of the paper "MotionCLIP: Exposing Human Motion Generation to CLIP Space". mathux/temos — Official PyTorch implementation of the paper "TEMOS: Generating diverse human motions from textual descriptions", ECCV… mathux/tmr — Mathis Petrovich · Michael J. Black · Gül Varol. mingyuan-zhang/motiondiffuse — MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model.