2 citations · 2 across the 16 of their papers we have counts for
16 papers
Multi-scale Coarse-to-fine Modeling for Test-time Human Motion Control
Nhat Le, Daochang Liu, Anh Nguyen +1
We present MSCoT, a multi-scale, coarse-to-fine model for test-time human motion synthesis and control. Unlike recent approaches that rely on multiple iterative denoising/token-pre…
Diffusion Masked Pretraining for Dynamic Point Cloud
Zhuoyue Zhang, Jihua Zhu, Yiding Sun +4
Dynamic point cloud pretraining is still dominated by masked reconstruction objectives. However, these objectives inherit two key limitations. Existing methods inject ground-truth…
Mantis: Mamba-native Tuning is Efficient for 3D Point Cloud Foundation Models
Zihao Guo, Jihua Zhu, Yiding Sun +3
Pre-trained 3D point cloud foundation models (PFMs) have demonstrated strong transferability across diverse downstream tasks. However, full fine-tuning these models is computationa…
Causal Reinforcement Learning for Complex Card Games: A Magic The Gathering Benchmark
Cristiano da Costa Cunha, Ajmal Mian, Tim French +1
Causal reinforcement learning (RL) lacks benchmarks for complex systems that combine sequential decision making, hidden information, large masked action spaces, and explicit causal…
GenHOI: Generalized Hand-Object Pose Estimation with Occlusion Awareness
Hui Yang, Wei Sun, Jian Liu +6
Generalized 3D hand-object pose estimation from a single RGB image remains challenging due to the large variations in object appearances and interaction patterns, especially under…
Class-Partitioned VQ-VAE and Latent Flow Matching for Point Cloud Scene Generation
Dasith de Silva Edirimuni, Ajmal Saeed Mian
Most 3D scene generation methods are limited to only generating object bounding box parameters while newer diffusion methods also generate class labels and latent features. Using o…
Unifying Causal Reinforcement Learning: Survey, Taxonomy, Algorithms and Applications
Cristiano da Costa Cunha, Wei Liu, Tim French +1
Integrating causal inference (CI) with reinforcement learning (RL) has emerged as a powerful paradigm to address critical limitations in classical RL, including low explainability,…
ARMFlow: AutoRegressive MeanFlow for Online 3D Human Reaction Generation
Zichen Geng, Zeeshan Hayder, Wei Liu +2
3D human reaction generation faces three main challenges:(1) high motion fidelity, (2) real-time inference, and (3) autoregressive adaptability for online scenarios. Existing metho…
Enhancing Physical Plausibility in Video Generation by Reasoning the Implausibility
Yutong Hao, Chen Chen, Ajmal Saeed Mian +2
Diffusion models can generate realistic videos, but existing methods rely on implicitly learning physical reasoning from large-scale text-video datasets, which is costly, difficult…
Hybrid Long and Short Range Flows for Point Cloud Filtering
Dasith de Silva Edirimuni, Xuequan Lu, Ajmal Saeed Mian +4
Point cloud capture processes are error-prone and introduce noisy artifacts that necessitate filtering/denoising. Recent filtering methods often suffer from point clustering or noi…
UniHM: Universal Human Motion Generation with Object Interactions in Indoor Scenes
Zichen Geng, Zeeshan Hayder, Wei Liu +1
Human motion synthesis in complex scenes presents a fundamental challenge, extending beyond conventional Text-to-Motion tasks by requiring the integration of diverse modalities suc…
MonoDiff9D: Monocular Category-Level 9D Object Pose Estimation via Diffusion Model
Jian Liu, Wei Sun, Hui Yang +4
Object pose estimation is a core means for robots to understand and interact with their environment. For this task, monocular category-level methods are attractive as they require…
Auto-Regressive Diffusion for Generating 3D Human-Object Interactions
Zichen Geng, Zeeshan Hayder, Wei Liu +1
Text-driven Human-Object Interaction (Text-to-HOI) generation is an emerging field with applications in animation, video games, virtual reality, and robotics. A key challenge in HO…
Generative Physical AI in Vision: A Survey
Daochang Liu, Junyu Zhang, Anh-Dung Dinh +5
Generative Artificial Intelligence (AI) has rapidly advanced the field of computer vision by enabling machines to create and interpret visual data with unprecedented sophistication…
Motion-Grounded Video Reasoning: Understanding and Perceiving Motion at Pixel Level
Andong Deng, Tongjia Chen, Shoubin Yu +6
In this paper, we introduce Motion-Grounded Video Reasoning, a new motion understanding task that requires generating visual answers (video segmentation masks) according to the inp…
Avatar Concept Slider: Controllable Editing of Concepts in 3D Human Avatars
Lin Geng Foo, Yixuan He, Ajmal Saeed Mian +3
Text-based editing of 3D human avatars to precisely match user requirements is challenging due to the inherent ambiguity and limited expressiveness of natural language. To overcome…