Jia, Sen, Wang, Huayu, Huang, Hsiang-Wei, An, Zhaochong, Hwang, Jenq-Neng, Zhang, Huaping, Li, Lei
Aligning natural language descriptions with precise 3D human poses remains a big challenge due to the scarcity of effective pose representation mechanisms and large-scale, semantically rich datasets. To overcome these limitations, we first introduce **CLEP-2M**, the largest 3D pose-language dataset to date, comprising two million high-quality 3D pose-language pairs. This dataset provides a **20-fold** increase in scale and far richer semantic diversity than existing benchmarks. Second, we propose **CLEP**, a novel contrastive pretraining framework. The core of CLEP is HierFormer, a hierarchical pose encoder specifically designed for language alignment. Its key innovation is a Cross-Scale Attention Fusion (CSAF) mechanism that dynamically integrates features from the joint, limb, and body levels. This enables CLEP to precisely align complex, multi-scale text descriptions with the pose representation. Extensive experimental evaluations on CLEP-2M and PoseScript demonstrate that our method consistently outperforms existing approaches across a range of downstream tasks. CLEP shows exceptional zero-shot generalization, achieving a 34.8 mRecall on the human-annotated PoseScript-H benchmark--a nearly **6-fold** improvement from the baseline. Furthermore, CLEP demonstrates superior performance on pose generation and fine-grained pose editing. These results establish CLEP as a strong multimodal foundation model for human-centric understanding and generation tasks.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Huang, Xiaoke, Gauri, Bhavul, Ng, Kam Woh, Ng, Tony, Xu, Mengmeng, Liu, Zhiheng, Ren, Weiming, An, Zhaochong 等 15 位作者
Vector glyphs are the atomic units of digital typography, yet most learning-based pipelines still depend on carefully curated exemplar sheets and raster-to-vector postprocessing, which limits accessibility and editability. We introduce VecGlypher, a single multimodal language model that generates high-fidelity vector glyphs directly from text descriptions or image exemplars. Given a style prompt, optional reference glyph images, and a target character, VecGlypher autoregressively emits SVG path tokens, avoiding raster intermediates and producing editable, watertight outlines in one pass. A typography-aware data and training recipe makes this possible: (i) a large-scale continuation stage on 39K noisy Envato fonts to master SVG syntax and long-horizon geometry, followed by (ii) post-training on 2.5K expert-annotated Google Fonts with descriptive tags and exemplars to align language and imagery with geometry; preprocessing normalizes coordinate frames, canonicalizes paths, de-duplicates families, and quantizes coordinates for stable long-sequence decoding. On cross-family OOD evaluation, VecGlypher substantially outperforms both general-purpose LLMs and specialized vector-font baselines for text-only generation, while image-referenced generation reaches a state-of-the-art performance, with marked gains over DeepVecFont-v2 and DualVector. Ablations show that model scale and the two-stage recipe are critical and that absolute-coordinate serialization yields the best geometry. VecGlypher lowers the barrier to font creation by letting users design with words or exemplars, and provides a scalable foundation for future multimodal design tools.
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cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表