6 accepted papers
Universal sound separation faces a fundamental misalignment: models optimized for low-level signal metrics often produce semantically contaminated outputs, failing to suppress perceptually salient interference from acoustically similar sources. We introduce a preference alignment perspective, analog…
Recent advances in transformer-based text-to-motion generation have significantly improved motion quality. However, achieving both real-time performance and long-horizon scalability remains an open challenge. In this paper, we present MOGO (Motion Generation with One-pass), a novel autoregressive fr
Hallucinations present a significant challenge in the development and evaluation of large language models (LLMs), directly affecting their reliability and accuracy. While notable advancements have been made in research on textual and visual hallucinations, there is still a lack of a comprehensive be…
Extensive research on LLM-based spoken dialogue systems has significantly advanced the development of intelligent voice assistants. However, the integration of role information within speech remains an underexplored area, limiting its application in real-world scenarios, particularly in multi-party
With the rapid advancement of large models, voice assistants are gradually acquiring the ability to engage in open-ended daily conversations with humans. However, current spoken dialogue systems often overlook multi-modal information in audio beyond text, such as speech rate, volume, emphasis, and b…
Recent advancements in single image driven 3D content generation have been propelled by leveraging prior knowledge from pretrained 2D diffusion models. However the 3D content generated by existing methods often exhibits distorted outline shapes and inadequate details. To solve this problem we propos…