Hao Zhang

Estimation of cotton plant moisture content using UAV multimodal data and machine learning

Zhenyang Wang, Caixia Yin, Siyuan Chen, Hao Zhang, Maoguang Chen, Tao Lin, et al.

MagicMakeup: A Region-Controllable Diffusion Transformer for High-Fidelity Makeup-Transfer

Ziyi Wang, Siming Zheng, Yang Yang, Shusong Xu, Hao Zhang, Bo Li, et al.

Makeup-transfer applies the reference makeup to the source face while preserving the source identity. Despite advances in full-face editing by diffusion-based methods, strong regional controllability, makeup fidelity, and identity preservation remain challenging. The reasons are…Read more

Molt: A Scalable PyTorch-Native Training Framework for Agentic Reinforcement Learning

Jian Hu, Huiying Li, Hao Zhang, Binfeng Xu, Yifan Zhang, Shaokun Zhang, et al.

Agentic reinforcement learning research is constant algorithm modification, new estimators, new pipeline stages, new rollout schemes, and in mainstream frameworks each change threads through layers of trainer, distributed backend, and rollout glue: the cost lands on the researche…Read more

Local Causal Structure Learning in the Presence of Latent Variables and Selection Bias

Zheng Li, Hao Zhang, Ruxin Wang, Ruichu Cai, Kun Zhang, Feng Xie

Discovering the direct causes and effects of a target variable from observational data is a fundamental problem in causal discovery, with broad applications in domains such as gene regulatory analysis and biomedical research. Existing causal discovery methods either learn a globa…Read more

QIRF Quantum-Inspired Non-Orthogonal Function-Space Compression for 3D Gaussian Splatting

Shizeng Jiang, Hao Zhang, Xuerui Ma, Ying Hu, Tao Zhang

3D Gaussian Splatting (3DGS) achieves high-quality real-time rendering by representing a scene with a large collection of anisotropic Gaussian primitives. However, complex scenes often require millions of Gaussians, resulting in substantial storage and rendering costs. Existing c…Read more

Packet-Loss Robust 3D Gaussian Compression via Atomic Packaging and GNN-based Error Concealment

Yuxuan Tao, Xuerui Ma, Hao Zhang, Chunhua Peng

3D Gaussian Splatting (3DGS) and recent compression schemes such as HAC++ enable high-fidelity real-time neural rendering, but their bitstreams are fragile under packet loss during network streaming. Existing compression methods often separate correlated anchor attributes into in…Read more

CNS-Edit++: Category-Agnostic 3D Editing with Coupled Neural Shape Representation

Jingyu Hu, Weilong Yan, Zhengzhe Liu, Haipeng Li, Ka-Hei Hui, Hao Zhang, et al.

This paper presents a latent-space 3D shape editing framework built upon a coupled neural shape (CNS) representation and a neural feature volume optimization. This work extends CNS-Edit, built on Coupled Neural Shape optimization, to CNS-Edit++, by generalizing the category-speci…Read more

Beyond Entropy: Correctness-Aware Advantage Shaping via Contrastive Policy Optimization

Weiwen Xu, Jia Liu, Hou Pong Chan, Long Li, Deng Cai, Min Chen, et al.

Reinforcement learning with verifiable rewards (RLVR) commonly uses entropy for advantage shaping. However, entropy cannot distinguish useful uncertainty from detrimental confusion, limiting its effectiveness as a correctness signal. We propose Contrastive Policy Optimization (CP…Read more

Communication-Efficient Relative Pose Estimation with Vision Foundation Models for Ephemeral Collaborative Perception

Qihang Li, Jo-Hao Huang, Jiewen Liu, Suyoung Kang, Hao Zhang, Peng Gao

Relative pose estimation is a fundamental capability for collaborative perception and coordination in multi-robot systems. However, robots encountering each other in real-world environments often operate in short interaction windows and must operate under limited communication ba…Read more

RegHead: Non-Humanoid Head Blendshapes via Feed-Forward Registration

Jiahao Luo, Hao Zhang, Jianqi Chen, Yijie He, Jiaxu Zou, Michael Vasilkovsky, et al.

We present RegHead, a framework for constructing semantic blendshape sets for animatable non-humanoid head avatars. With a fixed expression vocabulary, semantic blendshapes provide a low-dimensional and interpretable animation interface and support cross-identity retargeting. Bui…Read more

Integrated Automated Car Following and Lane-changing control based on a Parametrized Deep Q-network with Hybrid Action Space

Hao Zhang, Zihao Li, Yang Zhou

Lane-change, a triggering of traffic disturbances to the upstream vehicles, is detrimental to traffic safety and efficiency. Coupled with car-following behavior, the joint maneuvers depict the general picture of how traffic disturbances generate and propagate through vehicle stre…Read more

SPEARBench: A Benchmark for Naturalness Evaluation in Streaming Speech-to-Speech Language Models

Thomas Thebaud, Yuzhe Wang, Hao Zhang, Sathvik Manikantan Napa Ugandhar, Ashish Hallur, Georgi Tinchev, et al.

Streaming speech-to-speech language models aim to answer spoken queries directly with synthetic speech. However, standard speech and text benchmarks do not capture whether these systems behave naturally in conversations, where timing, turn-taking, prosody, interpersonal stance, l…Read more

Semantic-Guided Progressive Object Removal with Gaussian Splatting

Xianliang Huang, Chen Xiao, Yuanxiang Ni, Guanming Liu, Mingkai Liu, Dikai Fan, et al.

Removing unwanted objects from reconstructed 3D scenes is an important task in computer vision, supporting applications in AR/VR, robotics, and digital content creation. Existing methods typically complete the entire masked region in a single step and without effectively utilizin…Read more

TRACE-EVC: Text-Guided Relative Affective Control for Zero-Shot Emotional Voice Conversion

Zihan Zhang, Shreeram Suresh Chandra, Zongyang Du, Xiutian Zhao, Aurosweta Mahapatra, Hao Zhang, et al.

Traditional emotional voice conversion (EVC) conditions generation on explicit target emotions like labels or references, defining the target affective state but omitting the direction or nature of the transition. We introduce instruction-guided relative emotional voice conversio…Read more

TurnNat: Automatic Evaluation of Turn-Taking Naturalness in Dyadic Spoken Dialogue

Hao Zhang, Thomas Thebaud, Georgi Tinchev, Venkatesh Ravichandran, Laureano Moro-Velazquez

Turn-taking naturalness is central to full-duplex spoken dialogue systems, yet its automatic evaluation remains limited. Existing evaluations often rely on human judgments or behavior-specific timing metrics, making it difficult to compare heterogeneous timing failures within a u…Read more

TRACE: Temporal Relationship-Aware Conversational Entrainment Detection in Dyadic Speech

Sathvik Manikantan Napa Ugandhar, Hao Zhang, Alison Gunzler, Yuzhe Wang, Thomas Thebaud, Georgi Tinchev, et al.

With the proliferation of speech AI agents, understanding emotional entrainment in conversational interaction has become increasingly important. Emotional entrainment is shaped by social relationships and conversational context, influencing affective coordination over time. We in…Read more