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...
InteractGesture is a model‑agnostic, inference‑time method that enables fine‑grained spatial control of individual joints in continuous streaming co‑speech gesture generation. It guides diffusion sampler latent estimates through a differentiable RVQ‑VAE decoder, backpropagating spatial control gradients to adjust motion latents during sampling. To address chunk‑wise dependency issues in streaming generation, the method introduces Progressive Chunk Guidance, a chunk‑window strategy that keeps an active set of editable chunk latents with staggered delays, allowing spatial constraints to propagate gradients backward across chunk boundaries and reducing boundary inconsistencies.
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.
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...
DuoGesture is a co‑speech gesture generation model that separates gesture synthesis into a semantic stream and a beat stream, coordinated by a Semantic Variational Information Bottleneck that decides when semantic gestures override rhythmic motion. The semantic stream uses Motion‑Grounded Semantic Conditioning, replacing word embeddings with motion‑language representations to provide motion‑aligned semantic priors for rare gesture triggers. The beat stream is regularised by an Inertial Beat Prior, an anthropometry‑weighted arm‑chain module that reduces jitter and improves rhythmic consistency. Experiments show DuoGesture outperforms strong baselines and ablations confirm the complementary roles of semantic grounding, stochastic stream selection, and biomechanical regularisation.
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...
Synthesizing realistic full-body human interactions with articulated objects is a fundamental challenge for embodied AI and graphics, with applications in robotics training and virtual agents. Existing models remain limited: some focus on simple activities with static objects, while others restrict attention to hand-only manipulation.
The paper introduces Hand2Bot, an RGB‑D video dataset designed for human‑to‑robot handover scenarios, capturing body posture and facial expressions amid real‑world noise. It also proposes PassGen, a generative pipeline using stable video diffusion and an Intention‑Aware Temporal Face Encoder to synthesize realistic handover sequences while maintaining hand‑object consistency. A morphology‑based depth editing strategy is employed to replicate realistic sensor noise, and experiments show that training on PassGen yields high intention identification accuracy, low false trigger rates, and robust zero‑shot transfer to a physical robot platform.
arXiv:2607. 17097v2 Announce Type: replace Abstract: Hand-Object Interaction (HOI) synthesis is a cornerstone for animation production and embodied AI.
arXiv:2608. 20312v1 Announce Type: new Abstract: The capability to perceive and synthesize human-human interactions is fundamental to developing intelligent digital human systems.
arXiv:2608. 16222v1 Announce Type: cross Abstract: Humanoid intelligence requires learning over an extremely diverse space of whole-body motions and physically grounded interactions.
arXiv:2607. 16322v1 Announce Type: cross Abstract: Micro-gesture recognition demands the detection of fleeting, spatially localized movements that are frequently overwhelmed by dominant static appearances and background noise.