15 citations · 42 across the 13 of their papers we have counts for
14 papers
Orbia: Do Generated Videos Form a Consistent 3D World?
Dongyue Lu, Guian Fang, Hur Lim +20
Video generators serve as interactive worlds for exploration and simulation, yet realistic and visually appealing frames do not necessarily depict a consistent 3D world. Existing b…
SPIRAL: Self-Evolving Action-Conditioned Video Generation via Reflective Planning Agents
Yu Yang, Yue Liao, Jianbiao Mei +11
Long-horizon action-conditioned video generation aims to synthesize temporally coherent videos that follow complex action instructions over extended horizons, requiring procedural…
PointDGRWKV: Generalizing RWKV-like Architecture to Unseen Domains for Point Cloud Classification
Hao Yang, Qianyu Zhou, Haijia Sun +4
Domain Generalization (DG) has been recently explored to enhance the generalizability of Point Cloud Classification (PCC) models toward unseen domains. Prior works are based on con…
JarvisArt: Liberating Human Artistic Creativity via an Intelligent Photo Retouching Agent
Yunlong Lin, Zixu Lin, Kunjie Lin +8
Photo retouching has become integral to contemporary visual storytelling, enabling users to capture aesthetics and express creativity. While professional tools such as Adobe Lightr…
Muddit: Liberating Generation Beyond Text-to-Image with a Unified Discrete Diffusion Model
Qingyu Shi, Jinbin Bai, Zhuoran Zhao +7
Unified generation models aim to handle diverse tasks across modalities -- such as text generation, image generation, and vision-language reasoning -- within a single architecture…
Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis
Jinbin Bai, Tian Ye, Wei Chow +5
We present Meissonic, which elevates non-autoregressive masked image modeling (MIM) text-to-image to a level comparable with state-of-the-art diffusion models like SDXL. By incorpo…
PointDGMamba: Domain Generalization of Point Cloud Classification via Generalized State Space Model
Hao Yang, Qianyu Zhou, Haijia Sun +5
Domain Generalization (DG) has been recently explored to improve the generalizability of point cloud classification (PCC) models toward unseen domains. However, they often suffer f…
Mamba or RWKV: Exploring High-Quality and High-Efficiency Segment Anything Model
Haobo Yuan, Xiangtai Li, Lu Qi +4
Transformer-based segmentation methods face the challenge of efficient inference when dealing with high-resolution images. Recently, several linear attention architectures, such as…
OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding
Tao Zhang, Xiangtai Li, Hao Fei +5
Current universal segmentation methods demonstrate strong capabilities in pixel-level image and video understanding. However, they lack reasoning abilities and cannot be controlled…
Reason3D: Searching and Reasoning 3D Segmentation via Large Language Model
Kuan-Chih Huang, Xiangtai Li, Lu Qi +2
Recent advancements in multimodal large language models (LLMs) have demonstrated significant potential across various domains, particularly in concept reasoning. However, their app…
DGMamba: Domain Generalization via Generalized State Space Model
Shaocong Long, Qianyu Zhou, Xiangtai Li +5
Domain generalization~(DG) aims at solving distribution shift problems in various scenes. Existing approaches are based on Convolution Neural Networks (CNNs) or Vision Transformers…
DVIS-DAQ: Improving Video Segmentation via Dynamic Anchor Queries
Yikang Zhou, Tao Zhang, Shunping Ji +2
Modern video segmentation methods adopt object queries to perform inter-frame association and demonstrate satisfactory performance in tracking continuously appearing objects despit…
Explore In-Context Segmentation via Latent Diffusion Models
Chaoyang Wang, Xiangtai Li, Henghui Ding +5
In-context segmentation has drawn increasing attention with the advent of vision foundation models. Its goal is to segment objects using given reference images. Most existing appro…
Point Cloud Mamba: Point Cloud Learning via State Space Model
Tao Zhang, Haobo Yuan, Lu Qi +5
Recently, state space models have exhibited strong global modeling capabilities and linear computational complexity in contrast to transformers. This research focuses on applying s…