6 accepted papers
Generating realistic human motions from textual descriptions has undergone significant advancements. However, existing methods often overlook specific body part movements and their timing. In this paper, we address this issue by enriching the textual description with more details. Specifically, we p…
Audio-driven human animation methods, such as talking head and talking body generation, have made remarkable progress in generating synchronized facial movements and appealing visual quality videos. However, existing methods primarily focus on single human animation and struggle with multi-stream au…
Recent motion-aware large language models have demonstrated promising potential in unifying motion comprehension and generation. However, existing approaches primarily focus on coarse-grained motion-text modeling, where text describes the overall semantics of an entire motion sequence in just a few…
Adverse weather conditions, such as rain, snow, and haze, introduce complex degradations that present substantial challenges for effective image restoration. Existing all-in-one models often rely on fixed network structures, limiting their ability to adapt to the varying characteristics of different…
The commercialization of generative artificial intelligence (GenAI) has led to a multi-level ecosystem involving model developers, service providers, and consumers. Thus, ensuring traceability is crucial, as service providers may violate intellectual property rights (IPR), and consumers may generate…
Document-level Event Extraction aims to identify events from an entire article. It is quite a challenging task because event arguments scatter across several sentences and multiple events in a document may have influence on each other. Previous methods, however, did not take advantage of document st…