SOSControl: Enhancing Human Motion Generation through Saliency-Aware Symbolic Orientation and Timing Control
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Abstract (arXiv)
Traditional text-to-motion frameworks often lack precise control, and existing approaches based on joint keyframe locations provide only positional guidance, making it challenging and unintuitive to specify body part orientations and motion timing. To address these limitations, we introduce the Salient Orientation Symbolic (SOS) script, a programmable symbolic framework for specifying body part orientations and motion timing at keyframes. We further propose an automatic SOS extraction pipeline that employs temporally-constrained agglomerative clustering for frame saliency detection and a Saliency-based Masking Scheme (SMS) to generate sparse, interpretable SOS scripts directly from motion data. Moreover, we present the SOSControl framework, which treats the available orientation symbols in the sparse SOS script as salient and prioritizes satisfying these constraints during motion generation. By incorporating SMS-based data augmentation and gradient-based iterative optimization, the framework enhances alignment with user-specified constraints. Additionally, it employs a ControlNet-based ACTOR-PAE Decoder to ensure smooth and natural motion outputs. Extensive experiments demonstrate that the SOS extraction pipeline generates human-interpretable scripts with symbolic annotations at salient keyframes, while the SOSControl framework outperforms existing baselines in motion quality, controllability, and generalizability with respect to motion timing and body part orientation control.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 25 references that are in the map. First posted on arXiv 14 Nov 2025.
Builds on
16 of 25 references.
References · 16
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2020Denoising Diffusion Implicit Modelscited by 9,781
- 2018On the Continuity of Rotation Representations in Neural Networkscited by 1,214
- 2022MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Modelcited by 780
- 2022Executing your Commands via Motion Diffusion in Latent Spacecited by 575
- 2021Action-Conditioned 3D Human Motion Synthesis with Transformer VAEcited by 501
- 2023Human Motion Diffusion as a Generative Priorcited by 357
- 2023OmniControl: Control Any Joint at Any Time for Human Motion Generationcited by 238
- 2023Guided Motion Diffusion for Controllable Human Motion Synthesiscited by 223
- 2023AttT2M: Text-Driven Human Motion Generation with Multi-Perspective Attention Mechanismcited by 117
- 2023FineMoGen: Fine-Grained Spatio-Temporal Motion Generation and Editingcited by 106
- 2023TLControl: Trajectory and Language Control for Human Motion Synthesiscited by 94
4 more
- 2023Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Modelcited by 79
- 2024CoMo: Controllable Motion Generation through Language Guided Pose Code Editingcited by 67
- 2023Act As You Wish: Fine-Grained Control of Motion Diffusion Model with Hierarchical Semantic Graphscited by 45
- 2023Bridging the Gap between Human Motion and Action Semantics via Kinematic Phrasescited by 13
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Citing papers per year: 2026: 1.
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