Chenyan Wu, Yandong Li, Xianfeng Tang, Zhe Huang, James Wang
Reconstructing multi-human body meshes from a single monocular image is a challenging problem, requiring both individual body meshes and relative 3D positions to produce a coherent scene representation. We introduce MUG (Multi-hUman Graph network), which uses a single graph neural network to construct coherent multi-human meshes from only 2D poses. Our approach demonstrates that pose-based methods can jointly perform depth reasoning and multi-human mesh generation. Existing image-based methods often rely on lab-collected datasets with accurate 3D labels, introducing domain gaps when applied to in-the-wild or art images due to differences in appearance and context. In contrast, MUG leverages the geometric consistency of 2D poses across diverse datasets to mitigate such discrepancies. MUG operates in three phases. First, it constructs a novel heterogeneous graph from multi-human 2D poses, connecting nodes across people and within individuals to capture inter-human interactions and body geometry, including skeletal and mesh structures. Second, a dual-branch graph neural network predicts inter-human depth relations and root-joint-relative mesh coordinates. Finally, these outputs are combined to construct complete multi-human 3D meshes. Despite using only 2D poses and a streamlined architecture, MUG outperforms existing multi-human mesh estimation methods across datasets from diverse domains, including RH, MuPoTS-3D, and 3DPW. Qualitative results show that MUG can handle art images, through-wall scenarios, and poor lighting conditions when paired with advanced 2D pose networks. Both qualitative and quantitative evaluations highlight its generalization ability in open-world scenarios.
Ziwen Xu Zhejiang University, Chenyan WU Zhejiang University of Technology, Hengyu Sun, Haiwen Hong Alibaba Group, Mengru Wang, Yunzhi Yao Zhejiang University, Longtao Huang Alibaba Group, Hui Xue 等 12 位作者
Methods for controlling large language models (LLMs), including local weight fine-tuning, LoRA-based adaptation, and activation-based interventions, are often studied in isolation, obscuring their connections and making comparison difficult. In this work, we present a unified view that frames these interventions as dynamic weight updates induced by a control signal, placing them within a single conceptual framework. Building on this view, we propose a unified preference-utility analysis that separates control effects into preference, defined as the tendency toward a target concept, and utility, defined as coherent and task-valid generation, and measures both on a shared log-odds scale using polarity-paired contrastive examples. Across methods, we observe a consistent trade-off between preference and utility: stronger control increases preference while predictably reducing utility. We further explain this behavior through an activation manifold perspective, in which control shifts representations along target-concept directions to enhance preference, while utility declines primarily when interventions push representations off the model’s valid-generation manifold. Finally, we introduce a new steering approach guided by this analysis that improves preference while better preserving utility.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表