1 citations · 2 across the 11 of their papers we have counts for
12 papers
StereoWorld: Geometry-Aware Monocular-to-Stereo Video Generation
Ke Xing, Xiaojie Jin, Longfei Li +8
The growing adoption of XR devices has fueled strong demand for high-quality stereo video, yet its production remains costly and artifact-prone. To address this challenge, we prese…
PanFlow: Decoupled Motion Control for Panoramic Video Generation
Cheng Zhang, Hanwen Liang, Donny Y. Chen +4
Panoramic video generation has attracted growing attention due to its applications in virtual reality and immersive media. However, existing methods lack explicit motion control an…
ATLAS: A High-Difficulty, Multidisciplinary Benchmark for Frontier Scientific Reasoning
Hongwei Liu, Junnan Liu, Shudong Liu +33
The rapid advancement of Large Language Models (LLMs) has led to performance saturation on many established benchmarks, questioning their ability to distinguish frontier models. Co…
PanoWorld-X: Generating Explorable Panoramic Worlds via Sphere-Aware Video Diffusion
Yuyang Yin, HaoXiang Guo, Fangfu Liu +7
Generating a complete and explorable 360-degree visual world enables a wide range of downstream applications. While prior works have advanced the field, they remain constrained by…
ControlEchoSynth: Boosting Ejection Fraction Estimation Models via Controlled Video Diffusion
Nima Kondori, Hanwen Liang, Hooman Vaseli +5
Synthetic data generation represents a significant advancement in boosting the performance of machine learning (ML) models, particularly in fields where data acquisition is challen…
TiP4GEN: Text to Immersive Panorama 4D Scene Generation
Ke Xing, Hanwen Liang, Dejia Xu +4
With the rapid advancement and widespread adoption of VR/AR technologies, there is a growing demand for the creation of high-quality, immersive dynamic scenes. However, existing ge…
DELTAv2: Accelerating Dense 3D Tracking
Tuan Duc Ngo, Ashkan Mirzaei, Guocheng Qian +5
We propose a novel algorithm for accelerating dense long-term 3D point tracking in videos. Through analysis of existing state-of-the-art methods, we identify two major computationa…
Beyond Masked and Unmasked: Discrete Diffusion Models via Partial Masking
Chen-Hao Chao, Wei-Fang Sun, Hanwen Liang +2
Masked diffusion models (MDM) are powerful generative models for discrete data that generate samples by progressively unmasking tokens in a sequence. Each token can take one of two…
SpikeDerain: Unveiling Clear Videos from Rainy Sequences Using Color Spike Streams
Hanwen Liang, Xian Zhong, Wenxuan Liu +4
Restoring clear frames from rainy videos presents a significant challenge due to the rapid motion of rain streaks. Traditional frame-based visual sensors, which capture scene conte…
Wonderland: Navigating 3D Scenes from a Single Image
Hanwen Liang, Junli Cao, Vidit Goel +6
How can one efficiently generate high-quality, wide-scope 3D scenes from arbitrary single images? Existing methods suffer several drawbacks, such as requiring multi-view data, time…
Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models
Hanwen Liang, Yuyang Yin, Dejia Xu +5
The availability of large-scale multimodal datasets and advancements in diffusion models have significantly accelerated progress in 4D content generation. Most prior approaches rel…
Comp4D: LLM-Guided Compositional 4D Scene Generation
Dejia Xu, Hanwen Liang, Neel P. Bhatt +4
Recent advancements in diffusion models for 2D and 3D content creation have sparked a surge of interest in generating 4D content. However, the scarcity of 3D scene datasets constra…