Aug 2026· Computer graphics forum (Print)· 0 citations· 29 references
TL;DR
This work introduces a novel motion prior based on the sparsity of high‐order temporal derivatives, serving as a kinematic proxy for impulsive force generation and achieves linear complexity, enabling efficient processing of long sequences.
Abstract
Conventional human motion denoising methods often prioritize smoothness at the cost of dynamic fidelity. This aggressive smoothing tends to obliterate sharp transients, thereby diminishing the perceived forcefulness (or ‘Sense of Force') of the motion—a critical attribute for realistic animation. Inspired by physiological findings on RFD, we introduce a novel motion prior based on the sparsity of high‐order temporal derivatives. Specifically, we formulate denoising as an optimization problem that encourages jerk sparsity, serving as a kinematic proxy for impulsive force generation. Our solver achieves linear complexity, enabling efficient processing of long sequences. Extensive experiments on synthetic and real MoCap data demonstrate that our method effectively eliminates noise while preserving high‐frequency acceleration details more effectively compared to state‐of‐the‐art filtering and learning‐based approaches. User studies with professional animators confirm that our results are significantly preferred for their preserved dynamic impact. Furthermore, we showcase the versatility of our approach in applications such as forcefulness restoration and controllable editing. Our code will be made publicly available at
https://github.com/ChambinLee/sparse‐jerk‐mocap‐denoising
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This work proposes FlashMo, a frequency-aware sparse motion diffusion model that prunes low-frequency tokens to enhance efficiency without custom kernel design, and introduces MotionSiT, a scalable diffusion transformer based on a joint-temporal factorized interpolant with Lie group geodesics over SO(3) manifolds, enabling principled generation of joint rotations.
Zeyu Zhang, Yiran Wang, Danning Li et al.· Advances in Neural Informati...· 11 citations
F3AMD (Fast FiLM‐conditioned Fourier Autoregressive Motion Diffusion), a framework that achieves an order of magnitude speedup over state‐of‐the‐art systems for multi‐character animation on both GPUs and CPUs while maintaining high motion quality, is introduced.
Calvin Qiao, Benjamin MacAdam, Mohammadarsh Khokhar et al.· Computer graphics forum (Pri...· 0 citations
Motion warping is a core technique in character animation that enables the adaptation of existing motion data to novel spatio-temporal constraints. Conventional motion warping methods often rely on heuristic modifications that can violate physical consistency or introduce visual artifacts. More recent learning-based editing approaches improve realism, but many of them encode motion into tightly entangled latent space, which makes them struggle to balance editing flexibility and content preservation. To address this, we propose a novel deep motion warping framework that explicitly disentangles the motion structure from global and stylistic attributes for intuitive motion editing. Our key insight is to leverage learned phase features as a continuous and robust representation of the underlying structure, and explicitly disentangle motion into root velocity, phase, and learned latent variables using a phase-conditioned diffusion autoencoder. This design supports a wide range of editing operations, including root motion warping, motion exaggeration, time warping, and style transfer by directly manipulating decoupled components, without requiring paired training data. Extensive experiments demonstrate that our approach enables high-level, flexible motion editing while strictly preserving the structural consistency and physical plausibility of the source motion
Bowen Zheng, Linjun Wu, Xinwei Jiang et al.· International Conference on...· 0 citations
It is demonstrated that Laplacian editing produces stable and visually coherent transitions under a wide range of motion differences, and the proposed framework is well suited not only for animation authoring but also for motion analysis and future extensions incorporating perceptual or physiological cues.
Ryosuke Higasayama, Hideki Todo, Jongseong Gwak· 0 citations
This work proposes MeanSR, a one-step perceptual SR method that learns an LR-conditioned average velocity field to directly capture the finite-time transition from degraded or noisy inputs to plausible HR outputs and introduces a Stage-Aware Temporal Sampling strategy to improve trajectory learning.
Axi Niu, Jiawei Kou, Kang Zhang et al.· 0 citations
We present PhysDiff-VTON, a diffusion-based framework for image-based virtual try-on that systematically addresses the dual challenges of garment deformation modeling and high-frequency detail preservation. The core innovation lies in integrating physics-inspired mechanisms into the diffusion process: a pose-guided deformable warping module simulates fabric dynamics by predicting spatial offsets conditioned on human pose semantics, while wavelet-enhanced feature decomposition explicitly preserves texture fidelity through frequency-aware attention. Further enhancing generation quality, a novel sampling strategy optimizes the de-noising trajectory via least action principles, enforcing temporal coherence, spatial smoothness, and multi-scale structural consistency. Comprehensive evaluations across multiple datasets demonstrate significant improvements in both geometric plausibility and perceptual quality compared to existing approaches. The framework establishes a new paradigm for synthesizing photorealistic try-on images that adhere to physical constraints while maintaining intricate garment details, advancing the practical applicability of diffusion models in fashion technology.
Shibin Mei, Bingbing Ni· Advances in Neural Informati...· 1 citation