1 citations · 2 across the 5 of their papers we have counts for
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HOIGS: Human-Object Interaction Gaussian Splatting
Taewoo Kim, Suwoong Yeom, Jaehyun Pyun +6
Reconstructing dynamic scenes with complex human-object interactions is a fundamental challenge in computer vision and graphics. Existing Gaussian Splatting methods either rely on…
SeaCache: Spectral-Evolution-Aware Cache for Accelerating Diffusion Models
Jiwoo Chung, Sangeek Hyun, MinKyu Lee +5
Diffusion models are a strong backbone for visual generation, but their inherently sequential denoising process leads to slow inference. Previous methods accelerate sampling by cac…
Coherent Human-Scene Reconstruction from Multi-Person Multi-View Video in a Single Pass
Sangmin Kim, Minhyuk Hwang, Geonho Cha +2
Recent advances in 3D foundation models have led to growing interest in reconstructing humans and their surrounding environments. However, most existing approaches focus on monocul…
Decomposed Attention Fusion in MLLMs for Training-Free Video Reasoning Segmentation
Su Ho Han, Jeongseok Hyun, Pilhyeon Lee +3
Multimodal large language models (MLLMs) demonstrate strong video understanding by attending to visual tokens relevant to textual queries. To directly adapt this for localization i…
Multi-Granular Spatio-Temporal Token Merging for Training-Free Acceleration of Video LLMs
Jeongseok Hyun, Sukjun Hwang, Su Ho Han +6
Video large language models (LLMs) achieve strong video understanding by leveraging a large number of spatio-temporal tokens, but suffer from quadratic computational scaling with t…
Prototypes are Balanced Units for Efficient and Effective Partially Relevant Video Retrieval
WonJun Moon, Cheol-Ho Cho, Woojin Jun +5
In a retrieval system, simultaneously achieving search accuracy and efficiency is inherently challenging. This challenge is particularly pronounced in partially relevant video retr…