Smplx Collection

◆

◆

◆

◆

🔮Psychic

★★

“[CVPR'22] ICON: Implicit Clothed humans Obtained from Normals”

◆

◆

◆

◆

🔮Psychic

★★

“[CVPR'23, Highlight] ECON: Explicit Clothed humans Optimized via Normal integration”

◆

◆

◆

◆

🔮Psychic

★★

“[CVPR 2023] Official implementation of the paper "One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer"”

◆

◆

◆

◆

🔮Psychic

★★

“ECCV2020 - Official code repository for the paper : STAR - A Sparse Trained Articulated Human Body Regressor”

◆

◆

◆

◆

🔮Psychic

★★

“ExPose - EXpressive POse and Shape rEgression”

◆

◆

◆

◆

🔮Psychic

★

“[CVPR 2023] Official repository for downloading, processing, visualizing, and training models on the ARCTIC dataset.”

◆

◆

◆

◆

🔮Psychic

★

“[ICML 2024] 🍅HumanTOMATO: Text-aligned Whole-body Motion Generation”

◆

◆

◆

◆

🔮Psychic

★

“[TPAMI 2023] PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images”

◆

◆

◆

◆

🔮Psychic

★

“The Fast Way From Vertices to Parametric 3D Humans”

◆

◆

◆

◆

🔮Psychic

★

“(ECCV 2024) SignAvatars: A Large-scale 3D Sign Language Holistic Motion Dataset and Benchmark”

◆

◆

◆

◆

🔮Psychic

★

“Our model BUDDI learns the joint distribution of interacting people”

◆

◆

◆

◆

🔮Psychic

★

“[CVPR 2024] AMUSE: Emotional Speech-driven 3D Body Animation via Disentangled Latent Diffusion ”

◆

◆

◆

◆

🔮Psychic

★

“[ICCV 2025 Highlight] ETCH: Generalizing Body Fitting to Clothed Humans via Equivariant Tightness”

◆

◆

◆

◆

🔮Psychic

★

“Fit an SMPL body model to a scan”

◆

◆

◆

◆

📦Normal

★

“Towards Robust 3D Body Mesh Inference of Partially-observed Humans: semester project at ETH Zurich”

◆

◆

◆

◆

📦Normal

★

“OpenXRLab GPU Cloth Simulation Engine”

◆

◆

◆

◆

🔮Psychic

★

“Smplify-X implementation. (2025. 04. 07 No Error & Recent version)”

◆

◆

◆

◆

🔮Psychic

★

“Official PyTorch implementation of BLADE: Single-view Body Mesh Estimation through Accurate Depth Estimation (CVPR 2025). BLADE tackles close-range human mesh recovery where perspective distortion is strongest, and solves for camera pose and focal length in addition to SMPL(-X) parameters.”