PAGE (Parts-Aware Guidance) is a diffusion-based framework that improves co-speech gesture portrait video generation by enhancing local spatial fidelity and global audio‑visual consistency. It introduces Parts‑Specific Reweighting during training to focus supervision on key regions like lips, eyes, and hands, and Consistency Classifier Guidance plus Differential Guidance during inference to correct audio‑visual mismatches and refine motion separately. The authors also present the Chinese Announcer Speech (CAS) dataset, a large‑scale Chinese benchmark that expands on the PATS dataset, and demonstrate that PAGE outperforms existing methods in perceptual quality, gesture diversity, and audio‑visual alignment.
By Yabiao Wang, Jiafu Wu, Yicheng Xu, Teng Hu, Shengzhe Zhou, Yanxiao Sun, Jiangning Zhang, Ying Tai, Yong Liu, Ming-Hsuan Yang
The paper introduces a real‑time framework for generating co‑speech gestures for digital humans, coupling a streaming speech response module with a causal multimodal autoregressive gesture generator that uses only current speech and motion history. It also presents an offline data synthesis pipeline for virtual companion dialogues and a self‑evolving training loop that incorporates user feedback to continually adapt the model. Experiments show the system achieves a better latency‑quality trade‑off, stronger speech‑motion synchronization, and higher user preference than existing baselines.
By Wentao Jiang, Youchen Xie, Haidi Fan, Yajing Chen, Xin Wang, Ye Shi, Jingya Wang
arXiv:2609.00369v1 Announce Type: new
Abstract: Generating co-speech gestures that are temporally coherent, semantically aligned with speech, and grounded with surrounding objects remains challenging...
By Vida Adeli, Soroush Mehraban, Jacob Rommann, Harrison Sanborn, Cole Clifford, Babak Taati
GestureFAR is a flow‑autoregressive framework that generates natural co‑speech gestures from streaming speech while preserving causality and continuous motion expressiveness. It autoregresses over continuous motion latents using a transformer for audio‑motion context and a flow‑matching head to sample the next latent. A head‑only flow distillation strategy further reduces latency by collapsing multi‑step flow sampling into a single network evaluation, enabling real‑time token‑causal generation with improved quality‑latency trade‑off on the BEAT2 benchmark.
By Pinxin Liu, Haiyang Liu, Jiahao Luo, Junhua Huang, Chunhao Zou, Luchuan Song
Generating co-speech gestures that are temporally coherent, semantically aligned with speech, and grounded with surrounding objects remains challenging. Prior speech-driven gesture models emphasize au...
arXiv:2604.09057v3 Announce Type: replace
Abstract: Audio-video (AV) generation has recently made strong progress in perceptual quality and multimodal coherence, yet generating content with plausible...
By Junchao Liao, Zhenghao Zhang, Xiangyu Meng, Litao Li, Ziying Zhang, Siyu Zhu, Long Qin, Weizhi Wang
Sonic-AMG is a novel framework that generates human motion in real time from spatial audio, the first to do so in a streaming manner. It encodes acoustic content and body-relative source geometry as separate tokens, then uses a causal transformer with a flow head to produce continuous motion latents that a causal decoder turns into motion segments. The system introduces rehearsal training and spatial feedback to mitigate training‑inference mismatches, achieving state‑of‑the‑art FID and R‑precision on the SAM benchmark while enabling coherent, real‑time reactions to sound events and source locations.
By Jingcheng Ni, Zekun Li, Zhiyang Dou, Xiaoyan Cong, Ziyao Zeng, Hongyang Du, Randall Balestriero, Taku Komura, Srinath Sridhar
arXiv:2608.28693v1 Announce Type: cross
Abstract: Enabling humanoid robots to respond to human speech with synchronized and semantically meaningful gestures is fundamental to natural human-robot inte...
By Zifan Wang, Ziang Ren, Pengyang Shi, Zirui Wang, Chenghuai Lin, Tianze Wang, Zekun Qi, Liangliang Zhao, He Wang, Li Yi
The paper introduces MOCO, a diffusion-based framework that generates 3D avatar motions from concurrent multimodal inputs such as speech audio, text descriptions, and trajectory data. MOCO decouples motion generation by independently producing modality-specific motions at each denoising step and then assembling them according to spatial rules, iteratively refining the combined motion. This approach yields coherent, lifelike, and synchronized movements, outperforming existing baselines on a multimodal benchmark.
By Yifei Liu, Qiong Cao, Hongwei Yi, Huaiguang Jiang, Changxing Ding
arXiv:2610.08760v1 Announce Type: cross
Abstract: Recent advances in world models have enabled increasingly realistic visual synthesis. However, these generated environments remain largely silent. Br...
By Pengjun Fang, Jingyi Fa, Kam Man Wu, Jiaming Wang, Haoyuan Huang, Yaguang Wu, Xiangjun Huang, Ziyang Ma, Weijia Chen, Hongyu Liu, Zeyue Tian, Qifeng Chen
arXiv:2609.23797v1 Announce Type: cross
Abstract: Human motion is shaped by both external acoustic events and behavioral intent: spatial audio conveys environmental cues that elicit or guide a respon...
By Shuyang Xu, Zhiyang Dou, Yiduo Hao, Zekun Li, Liang Pan, Jingbo Wang, Cheng Lin, Yuan Liu, Wenping Wang, Mingmin Zhao, Taku Komura
arXiv:2608.23279v1 Announce Type: new
Abstract: Text-driven human motion synthesis has made substantial development with two core modules of motion representation and generative architecture. For rep...
By Chengqun Yang, Liang Xu, Yanping Li, Fulong Liu, Jingnan Gao, Weili Zeng, Yichao Yan