arXiv:2608.24334v1 Announce Type: new Abstract: Discrete motion representations have substantially advanced autoregressive text-to-motion generation. However, most motion tokenizers are optimized for...
MoVT is a new framework for text‑to‑motion generation that uses a cross‑modal augmented motion tokenizer to project 3D motion tokens into 2D, enriching the motion codebook with real‑world video patterns. The enriched tokens are mapped back to 3D, creating aligned 3D and 2D codebooks that better capture intricate motions. These codebooks feed a generative masked transformer, which predicts masked motion tokens in a modality‑agnostic way, allowing text‑index pairs from the 2D codebook and annotated videos to further improve generation quality. Empirical tests show MoVT outperforms previous state‑of‑the‑art methods on several key metrics.
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...
arXiv:2607. 08741v1 Announce Type: cross Abstract: Generating realistic 3D human motions in real-time within interactive applications is key for animation, simulation, and humanoid robotics.
arXiv:2603.08590v4 Announce Type: replace Abstract: Text-to-motion generation has advanced with larger corpora and stronger generators, yet many models still rely on holistic frame- or clip-level lat...
arXiv:2609.14615v1 Announce Type: cross Abstract: Unified motion generation and understanding is crucial for embodied AI systems that can both synthesize and interpret human actions in open-world env...
arXiv:2605. 29488v2 Announce Type: replace-cross Abstract: Conditional human motion generation remains a fundamental challenge in computer vision and robotics.
BiMoGen introduces a unified masked discrete diffusion framework for bidirectional motion‑text generation, addressing the limitations of autoregressive models in capturing bidirectional dependencies between language and motion. The approach employs a two‑stage training strategy—decoupled uni‑ and cross‑modal pretraining followed by supervised fine‑tuning—to establish robust cross‑modal correspondence, and incorporates Generation‑Aware Self‑Correction to mitigate error propagation during inference. Experiments on HumanML3D and KIT‑ML show competitive performance on both text‑to‑motion and motion‑to‑text tasks, demonstrating the effectiveness of the proposed training and correction mechanisms.
Uni-HOI is a unified framework that learns the joint distribution among text, human motion, and object motion for 4D human‑object interaction (HOI). It uses large language models and two motion‑specific VQ‑VAEs to convert heterogeneous motion data into token sequences, enabling seamless integration of all three modalities. A two‑stage training strategy first captures correlations on a large‑scale HOI dataset and then fine‑tunes for specific tasks, achieving strong performance on text‑driven HOI generation, object‑motion‑driven human motion generation, and human‑motion‑driven object motion prediction.
The paper introduces a promptable localized motion representation that generates persistent embeddings for user-specified regions in a video, without cropping or masking the input. By conditioning motion encoding directly on spatial masks while processing the full video, the method produces temporally consistent, region-addressable embeddings that capture local dynamics while preserving global context. These embeddings enable object-level motion transfer for dynamic scene composition and improve localized action classification in multi-actor videos, outperforming global representations that rely on cropping or post-hoc masking.
arXiv:2603. 22282v2 Announce Type: replace-cross Abstract: We present UniMotion, to our knowledge the first unified framework for simultaneous understanding and generation of human motion, natural language, and RGB images within a single architecture.
arXiv:2608.23258v1 Announce Type: new Abstract: We propose HetSkills, a novel framework designed to progressively learn heterogeneous skills within a unified latent space for physics-based character...