Recently, Tencent AI Lab and Tencent PCG's ARC Lab jointly launched a new framework called StereoCrafter, which can convert ordinary 2D videos into high-fidelity stereoscopic 3D videos. This innovation responds to the growing demand for 3D content, especially in the field of immersive experiences. StereoCrafter fully leverages the advantages of foundational models, overcoming the limitations of traditional conversion methods, significantly enhancing the generation effects, and ensuring that the generated content meets expectations.

Tencent announced today that its newly developed AI Smart Workspace ima.copilot (abbreviated as ima) is officially open to users in its Windows version. This product not only offers search capabilities but also answers user questions, creates text, and generates images, showcasing Tencent's in-depth exploration and technological strength in the field of artificial intelligence.

With significant advancements in text-to-video generation technology, how to generate semantic and temporally consistent audio content from video input has become a hot topic among researchers. Recently, the research team from Tencent's AI Lab has launched a new model called "Implicit Alignment Video to Audio Generation" - VTA-LDM, which aims to provide an efficient audio generation solution.Project Access: https://top.aibase.com/tool/vta-ldm The core concept of the VTA-LDM model is to use impli

Recently, Tencent AI Lab has introduced a new multi-agent framework called "TRANSAGENTS", specifically designed for translating ultra-long literary content. This framework is set to revolutionize the field of literary translation, offering readers enhanced cross-cultural reading experiences.This virtual translation publishing company simulates the real translation process, including roles such as senior editors and junior editors, to achieve more precise and efficient literary translation. It is

Large model hallucinations can be mainly divided into three categories: those that conflict with input, context, and facts. Compared to traditional models, large model hallucination evaluation faces new challenges such as large data scales, strong generalizability, and subtlety. Mitigating hallucinations can be approached from pre-training, fine-tuning, and reinforcement learning, but reliable evaluation remains a challenge. Overall, the evaluation and mitigation of large model hallucinations require further research to promote the practical application of large models.