Snap presents VIMI. Grounding Video Generation through Multi-modal Instruction. Existing text-to-video diffusion models rely solely on text-only encoders for their pretraining. This limitation stems from the absence of large-scale multimodal prompt video datasets, resulting in a

Stop by our @CVPR posters to meet the team!. We present 7 poster today, 2 papers are highlights. Video generation, 3D scene generation, 4D generation, improving quality of synthesized images and more!

💫 Happy to share MoA: a *Mixture-of-Attention* architecture for personalization of generative models! . Imagine YOU🫵🏻 drinking boba🧋in a night market, or scuba diving🤿, and more! . 📕: 🔗: 🎥:

Snap presents Evaluating Very Long-Term Conversational Memory of LLM Agents. Existing works on long-term open-domain dialogues focus on evaluating model responses within contexts spanning no more than five chat sessions. Despite advancements in long-context large language models

Snap Video. Scaled Spatiotemporal Transformers for Text-to-Video Synthesis. Contemporary models for generating images show remarkable quality and versatility. Swayed by these advantages, the research community repurposes them to generate videos. Since video content is highly