11 accepted papers
Video Large Language Models (VideoLLMs), which adopt large language models for video understanding, have been demonstrated for single-shot videos. However, they usually struggle in multi-shot videos with frequent shot changes, varying camera angles, etc., which makes VideoLLMs hardly answer question
Recent advances in text-to-video generation have sparked interest in generative video editing tasks. Previous methods often rely on task-specific architectures (e.g., additional adapter modules) or dedicated customizations (e.g., DDIM inversion), which limit the integration of versatile editing cond…
Unified multimodal understanding–generation models have shown promising results in image generation and editing, but remain largely constrained to the image domain. In this work, we present VOGUE, a versatile framework that extends unified modeling to the video domain. VOGUE adopts a dual-stream des…
Recently, the introduction of Chain-of-Thought (CoT) has largely improved generation ability of unified models. However, it is observed that the current thinking process during generation mainly focuses on the text consistency with the text prompt, ignoring the visual context consistency with the vi
Style control has been popular in video generation models. Existing methods often generate videos far from the given style, cause content leakage, and struggle to transfer one video to the desired style. Our first observation is that the style extraction stage matters, whereas existing methods empha…
The labeling difficulty has been a longstanding problem in deep image matting. To escape from fine labels, this work explores using rough annotations such as trimaps coarsely indicating the foreground/background as supervision. We present that the cooperation between learned semantics from indicated…
We introduce in-context matting a novel task setting of image matting. Given a reference image of a certain foreground and guided priors such as points scribbles and masks in-context matting enables automatic alpha estimation on a batch of target images of the same foreground category without additi…
"simplify the pipeline by discarding previous matching blocks, relying only pure self-attention for matching result in using only an MLP can directly regress the coordinates from the support keypoint. Category-Agnostic Pose Estimation (CAPE) aims to localize keypoints on an object of any category gi…
Automatic and interactive matting largely improve image matting by respectively alleviating the need for auxiliary input and enabling object selection. Due to different settings on whether prompts exist they either suffer from weakness in instance completeness or region details. Also when dealing wi…
We study the composition style in deep image matting, a notion that characterizes a data generation flow on how to exploit limited foregrounds and random backgrounds to form a training dataset. Prior art executes this flow in a completely random manner by simply going through the foreground pool or…
We introduce point affiliation into feature upsampling, a notion that describes the affiliation of each upsampled point to a semantic cluster formed by local decoder feature points with semantic similarity. By rethinking point affiliation, we present a generic formulation for generating upsampling k…