13 citations · 24 across the 15 of their papers we have counts for
17 papers
CineRecon: Benchmarking Frontier Models for Agentic Cinematic Reconstruction
Jialei Wang, Zehan Wang, XiaoYang Wu +6
Reconstructing reference videos as editable dynamic 3D scenes is an important path toward controllable video generation for filmmaking: these scenes provide modifiable, renderable…
SpatialReward: Verifiable Spatial Reward Modeling for Fine-Grained Spatial Consistency in Text-to-Image Generation
Sashuai Zhou, Qiang Zhou, Junpeng Ma +9
Recent advances in text-to-image (T2I) generation via reinforcement learning (RL) have benefited from reward models that assess semantic alignment and visual quality. However, most…
Orient Anything V2: Unifying Orientation and Rotation Understanding
Zehan Wang, Ziang Zhang, Jiayang Xu +5
This work presents Orient Anything V2, an enhanced foundation model for unified understanding of object 3D orientation and rotation from single or paired images. Building upon Orie…
Generative Reasoning Recommendation via LLMs
Minjie Hong, Zetong Zhou, Zirun Guo +5
Despite their remarkable reasoning capabilities across diverse domains, large language models (LLMs) face fundamental challenges in natively functioning as generative reasoning rec…
DSI-Bench: A Benchmark for Dynamic Spatial Intelligence
Ziang Zhang, Zehan Wang, Guanghao Zhang +5
Reasoning about dynamic spatial relationships is essential, as both observers and objects often move simultaneously. Although vision-language models (VLMs) and visual expertise mod…
APO: Enhancing Reasoning Ability of MLLMs via Asymmetric Policy Optimization
Minjie Hong, Zirun Guo, Yan Xia +4
Multimodal Large Language Models (MLLMs) are powerful at integrating diverse data, but they often struggle with complex reasoning. While Reinforcement learning (RL) can boost reaso…
GenSpace: Benchmarking Spatially-Aware Image Generation
Zehan Wang, Jiayang Xu, Ziang Zhang +4
Humans can intuitively compose and arrange scenes in the 3D space for photography. However, can advanced AI image generators plan scenes with similar 3D spatial awareness when crea…
Depth Anything with Any Prior
Zehan Wang, Siyu Chen, Lihe Yang +4
This work presents Prior Depth Anything, a framework that combines incomplete but precise metric information in depth measurement with relative but complete geometric structures in…
Orient Anything: Learning Robust Object Orientation Estimation from Rendering 3D Models
Zehan Wang, Ziang Zhang, Tianyu Pang +3
Orientation is a key attribute of objects, crucial for understanding their spatial pose and arrangement in images. However, practical solutions for accurate orientation estimation…
OmniSep: Unified Omni-Modality Sound Separation with Query-Mixup
Xize Cheng, Siqi Zheng, Zehan Wang +8
The scaling up has brought tremendous success in the fields of vision and language in recent years. When it comes to audio, however, researchers encounter a major challenge in scal…
MuVi: Video-to-Music Generation with Semantic Alignment and Rhythmic Synchronization
Ruiqi Li, Siqi Zheng, Xize Cheng +3
Generating music that aligns with the visual content of a video has been a challenging task, as it requires a deep understanding of visual semantics and involves generating music w…
WavTokenizer: an Efficient Acoustic Discrete Codec Tokenizer for Audio Language Modeling
Shengpeng Ji, Ziyue Jiang, Wen Wang +14
Language models have been effectively applied to modeling natural signals, such as images, video, speech, and audio. A crucial component of these models is the codec tokenizer, whi…
OmniBind: Large-scale Omni Multimodal Representation via Binding Spaces
Zehan Wang, Ziang Zhang, Hang Zhang +5
Recently, human-computer interaction with various modalities has shown promising applications, like GPT-4o and Gemini. Given the foundational role of multimodal joint representatio…
FreeBind: Free Lunch in Unified Multimodal Space via Knowledge Fusion
Zehan Wang, Ziang Zhang, Xize Cheng +8
Unified multi-model representation spaces are the foundation of multimodal understanding and generation. However, the billions of model parameters and catastrophic forgetting probl…
Extending Multi-modal Contrastive Representations
Zehan Wang, Ziang Zhang, Luping Liu +4
Multi-modal contrastive representation (MCR) of more than three modalities is critical in multi-modal learning. Although recent methods showcase impressive achievements, the high d…
Chat-3D: Data-efficiently Tuning Large Language Model for Universal Dialogue of 3D Scenes
Zehan Wang, Haifeng Huang, Yang Zhao +2
3D scene understanding has gained significant attention due to its wide range of applications. However, existing methods for 3D scene understanding are limited to specific downstre…
Connecting Multi-modal Contrastive Representations
Zehan Wang, Yang Zhao, Xize Cheng +8
Multi-modal Contrastive Representation learning aims to encode different modalities into a semantically aligned shared space. This paradigm shows remarkable generalization ability…