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Absolute Coordinates Make Motion Generation Easy
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Absolute Coordinates Make Motion Generation Easy
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State-of-the-art text-to-motion generation models rely on the kinematic-aware, local-relative motion representation popularized by HumanML3D, which encodes motion relative to the pelvis and to the previous frame with built-in redundancy. While this design simplifies training for earlier generation models, it introduces critical limitations for diffusion models and hinders applicability to downstream tasks. In this work, we revisit the motion representation and propose a radically simplified and long-abandoned alternative for text-to-motion generation: absolute joint coordinates in global space. Through systematic analysis of design choices, we show that this formulation achieves significantly higher motion fidelity, improved text alignment, and strong scalability, even with a simple Transformer backbone and no auxiliary kinematic-aware losses. Moreover, our formulation naturally supports downstream tasks such as text-driven motion control and temporal/spatial editing without additional task-specific reengineering and costly classifier guidance generation from control signals. Finally, we demonstrate promising generalization to directly generate SMPL-H mesh vertices in motion from text, laying a strong foundation for future research and motion-related applications.
Forward citations
Cited by 12 Pith papers
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Text Dictates, Music Decorates: Energy-based Attention for Editable Dance Motion Generation
STREAM decouples text and music conditioning in a diffusion transformer via AdaLN for structure and BEAM for beats, plus new Motorica++ dataset and editability metrics, claiming SOTA music alignment with preserved semantics.
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Marrying Text-to-Motion Generation with Skeleton-Based Action Recognition
CoAMD unifies skeleton-based action recognition and text-to-motion generation through autoregressive diffusion guided by a multi-modal recognizer, reporting SOTA results on 13 benchmarks for four tasks.
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Text Dictates, Music Decorates: Energy-based Attention for Editable Dance Motion Generation
STREAM decouples text (via AdaLN) from music (via energy-based BEAM attention) to generate editable, musically aligned dance motions with a new annotated dataset and editability metric.
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ARMS: Anchor-Relational Motion Streaming for Seamless Solo-Social Motion Transitions
A single causal diffusion model with an anchor–relational motion representation generates streaming solo and two-person motion and smooth solo–social transitions from incremental text.
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EgoForce: Robust Online Egocentric Motion Reconstruction via Diffusion Forcing
EgoForce reconstructs long-horizon full-body motion online from sparse noisy egocentric views by incrementally denoising with a temporally asymmetric diffusion schedule and noise-robust imputation.
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MoLingo: Motion-Language Alignment for Text-to-Human Motion Generation
A semantically aligned latent space plus multi-token cross-attention conditioning sets a new state of the art in text-to-human-motion generation on HumanML3D.
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IRG-MotionLLM: Interleaving Motion Generation, Assessment and Refinement for Text-to-Motion Generation
Interleaving motion generation with text-motion assessment and refinement improves alignment between generated human motion and goal text.
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FunPhase: A Periodic Functional Autoencoder for Motion Generation via Phase Manifolds
FunPhase encodes motion clips as sinusoidal phase functions and decodes them continuously in space and time, enabling reconstruction, generation, super-resolution, and body completion across skeletons.
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SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control
Scaling motion tracking models along size, data volume, and compute produces a foundation model for natural, robust humanoid whole-body control with downstream uses in kinematic planning and vision-language-action models.
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InterCMDM: Block-Causal Diffusion for Autoregressive Human Interaction Generation
InterCMDM proposes a block-causal latent diffusion framework with dual-stream causal transformers and multi-task attention masks for autoregressive text-conditioned two-person interaction generation and reports SOTA r...
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Coordinate-Based Dual-Constrained Autoregressive Motion Generation
CDAMD is a new autoregressive text-to-motion framework operating on continuous motion coordinates with dual constraints and diffusion-inspired components, establishing new benchmarks and claiming SOTA fidelity plus se...
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Exploring Motion-Language Alignment for Text-driven Motion Generation
MLA-Gen advances text-driven motion synthesis by aligning global motion patterns with fine-grained text semantics and mitigating attention sink effects via new masking techniques.
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