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Ekkasit Pinyoanuntapong

7 accepted papers

2026

LiveGesture: Streamable Co-Speech Gesture Generation Model

CVPR 2026

We propose LiveGesture, the first fully streamable, speech-driven full-body gesture generation framework that operates with zero look-ahead and supports arbitrary sequence length. Unlike existing co-speech gesture methods--which are designed for offline generation and either treat body regions indep

Cited by 0SourceScholar
2026

Walk Before You Dance: High-fidelity and Editable Dance Synthesis via Generative Masked Motion Prior

AAAI 2026technical

Recent advances in dance generation have enabled the automatic synthesis of 3D dance motions. However, existing methods still face significant challenges in simultaneously achieving high realism, precise dance-music synchronization, diverse motion expression, and physical plausibility. To address th

Cited by 0SourcePDFScholar
2025

GenHMR: Generative Human Mesh Recovery

AAAI 2025technical

Human mesh recovery (HMR) is crucial in many computer vision applications; from health to arts and entertainment. HMR from monocular images has predominantly been addressed by deterministic methods that output a single prediction for a given 2D image. However, HMR from a single image is an ill-posed…

Cited by 0SourcePDFScholar
2025

MaskControl: Spatio-Temporal Control for Masked Motion Synthesis

ICCV 2025poster

Recent advances in motion diffusion models have enabled spatially controllable text-to-motion generation. However, these models struggle to achieve high-precision control while maintaining high-quality motion generation. To address these challenges, we propose MaskControl, the first approach to intr…

2025

MaskHand: Generative Masked Modeling for Robust Hand Mesh Reconstruction in the Wild

ICCV 2025poster

Reconstructing a 3D hand mesh from a single RGB image is challenging due to complex articulations, self-occlusions, and depth ambiguities. Traditional discriminative methods, which learn a deterministic mapping from a 2D image to a single 3D mesh, often struggle with the inherent ambiguities in 2D-t…

2023

Gaitmixer: Skeleton-Based Gait Representation Learning Via Wide-Spectrum Multi-Axial Mixer

ICASSP 2023accepted

Most existing gait recognition methods are appearance-based, which rely on the silhouettes extracted from the video data of human walking activities. The less-investigated skeleton-based gait recognition methods directly learn the gait dynamics from 2D/3D human skeleton sequences, which are theoreti…

Cited by 0SourceScholar