Jul 2026· IEEE Transactions on Image Processing· Vol 35, pp. 8121-8136· 0 citations· 91 references
Medicine
Abstract
Motion quantization codebooks have been widely adopted to facilitate co-speech motion generation. However, the conventional quantization-based generation paradigm—which relies on probabilistic token sampling from limited discrete codebooks—suffers from two major limitations: crude, unreasonable motion representations and fixed, homogenized motion token sequences. To overcome these issues, we propose a novel explicit generation paradigm based on generative continuous quantization. Specifically, we first introduce a continuous quantization method to derive a set of generative motion units. This approach enables smoother and more accurate representation of human motion compared to classical methods. Building on these generative units, we further propose a compositional weight generation paradigm that replaces probabilistic sampling with deterministic, explicit motion synthesis. Moreover, as generalization capability is crucial for real-world deployment, we design a fully audio-aware encoder to extract style features that are decoupled from content. These features are integrated into the motion decoder via Adaptive Instance Normalization to enhance cross-speaker facial style generalization. Our method achieves state-of-the-art performance on two public datasets. Notably, owing to its concise and efficient architecture, our model attains an inference speed exceeding 4000 fps on the SHOW dataset, demonstrating strong potential for practical real-time applications.
Text-to-motion generation aims to synthesize semantically consistent and naturally coherent motion sequences from natural language descriptions. Given the continuous nature of human motion, diffusion models operating in a continuous latent space offer inherent advantages over vector quantization-based methods, particularly in avoiding quantization errors and in modeling quality. However, existing diffusion models primarily rely on mean squared error loss. This stepwise regression paradigm often leads to ‘over-smoothed’ motion sequences and struggles to capture the subtle semantic nuances embedded in textual descriptions. To realize the potential for continuous diffusion generation, an enhanced latent-space diffusion framework designed to elevate generation capabilities across two dimensions, namely, distribution approximation and semantic alignment, is proposed. Specifically, a latent-space adversarial discriminator is incorporated. By applying decoupled adversarial supervision, this component mitigates the detail loss caused by mean regression, significantly enhancing the physical realism and dynamic sharpness. Concurrently, a latent-space contrastive alignment strategy is introduced during the denoising process that reinforces the correspondence of the generated motion sequences with the given textual inputs via explicit cross-modal constraints. Extensive experiments on standard benchmarks demonstrate that the proposed method effectively addresses the limitations of conventional diffusion models, thus validating the potential of continuous diffusion frameworks within the domain of text-driven motion synthesis.
Zhaowu Li, Rui Liu, Deheng Zhu et al.· Visual Computing for Industr...· 0 citations
MUGEN is proposed, a unified motion--language framework that pays neither cost: no codebook, one draw, and surpasses the discrete-token state of the art on every retrieval and alignment metric on SnapMoGen.
Zhankai Ye, Yukai Jin, Bingyang Wei et al.· 0 citations
Text-driven human motion synthesis has made substantial development with two core modules of motion representation and generative architecture. For representation, Vector Quantization (VQ)-based methods compress motion data into discrete tokens while latent-based models operate directly in continuous space. However, both of these representations exhibit significant limitations. VQ-based methods suffer from inherent information loss, which compromises the quality, diversity, and generalization of generated motions, while continuous representation on holistic whole-body motion hinders part-level flexibility. For architecture, diffusion and autoregressive diffusion models have demonstrated their superiority, yet the fine-grained controllability over individual body parts is also limited. Thus, we propose a unified spatiotemporally decoupled framework named DeMoDiff, which jointly redesigns representation and architecture. To enhance representation extraction capabilities and offer greater part-level controllability, we present a spatial-temporal VAE that encodes each body joint rather than compressing the whole-body motion into a single latent space. Then, we incorporate spatial-temporal masking and attention mechanisms into an autoregressive diffusion generator, achieving both generative capability and controllable editability. Extensive experiments on the HumanML3D and KIT-ML datasets demonstrate that our model achieves state-of-the-art reconstruction performance and compelling motion generation results. Moreover, our framework demonstrates strong temporal and spatial editing capabilities, further validating its effectiveness. Our project page: https://rex0191.github.io/DeMoDiff/
Chengqun Yang, Liang Xu, Yanping Li et al.· 0 citations
Speech recordings often contain missing, corrupted, or incorrect regions that must be reconstructed or modified without re-synthesizing the entire utterance. Speech inpainting restores missing segments, whereas speech editing replaces spoken content according to an edited transcript. Both tasks require the generated speech to express the intended words while remaining consistent with the surrounding speaker identity, prosody, timing, and recording conditions. Discrete diffusion is particularly well suited to these tasks because it can iteratively refine masked tokens while jointly conditioning on both left and right acoustic context. We introduce SIEDD, a discrete diffusion framework for text-guided speech inpainting and editing over hierarchical codec tokens. Its core architecture, HiCoDD, follows the RVQ generation order by representing previously generated codebooks as clean, committed acoustic context and applying diffusion only to the current refinement codebook. This separation enables leakage-free joint training while matching sequential coarse-to-fine inference. The model further combines phoneme-level conditioning, span-localized classifier-free guidance, and duration prediction to support both fixed-duration inpainting and variable-duration text edits. On the RealEdit benchmark, SIEDD achieves the best overall speech-editing performance among the evaluated methods. It also outperforms the evaluated autoregressive baselines across all speech-inpainting settings, on both single and multiple gaps. These results demonstrate that explicitly modeling the codec hierarchy substantially improves context-preserving speech reconstruction and editing. See our full code at https://github.com/iftachShoham/SIEDD.
Iftach Shoham, Tali Dror, Oren Gal et al.· 0 citations