Seedance 1.0: Exploring the Boundaries of Video Generation Models

2025-06-10

AI Summary

Current video foundation models struggle to simultaneously achieve prompt fidelity, motion plausibility, and visual quality. To address this, we introduce the first high-performance video foundation model supporting unified text-to-video and image-to-video generation, as well as native multi-shot narrative synthesis. Methodologically, we construct a large-scale, precisely annotated multi-source video dataset; design an efficient architecture enabling joint multi-task training; and propose the first video-specific multidimensional RLHF framework coupled with a staged knowledge distillation pipeline. Our approach integrates diffusion modeling, multimodal alignment, fine-grained supervised fine-tuning, video-optimized reinforcement learning, and system-level inference optimization. On an NVIDIA L20 GPU, our model generates 1080p, 5-second videos in just 41.4 seconds—approximately 10× faster than state-of-the-art methods—while significantly improving spatiotemporal coherence, structural stability, and complex instruction following.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Notable breakthroughs in diffusion modeling have propelled rapid improvements in video generation, yet current foundational model still face critical challenges in simultaneously balancing prompt following, motion plausibility, and visual quality. In this report, we introduce Seedance 1.0, a high-performance and inference-efficient video foundation generation model that integrates several core technical improvements: (i) multi-source data curation augmented with precision and meaningful video captioning, enabling comprehensive learning across diverse scenarios; (ii) an efficient architecture design with proposed training paradigm, which allows for natively supporting multi-shot generation and jointly learning of both text-to-video and image-to-video tasks. (iii) carefully-optimized post-training approaches leveraging fine-grained supervised fine-tuning, and video-specific RLHF with multi-dimensional reward mechanisms for comprehensive performance improvements; (iv) excellent model acceleration achieving ~10x inference speedup through multi-stage distillation strategies and system-level optimizations. Seedance 1.0 can generate a 5-second video at 1080p resolution only with 41.4 seconds (NVIDIA-L20). Compared to state-of-the-art video generation models, Seedance 1.0 stands out with high-quality and fast video generation having superior spatiotemporal fluidity with structural stability, precise instruction adherence in complex multi-subject contexts, native multi-shot narrative coherence with consistent subject representation.

Problem

• Balancing prompt following, motion plausibility, and visual quality in video generation
• Improving multi-shot generation and joint learning of text-to-video and image-to-video tasks
• Achieving high-quality, fast video generation with spatiotemporal fluidity and structural stability

Innovation

• Multi-source data curation with precision captioning
• Efficient architecture supporting multi-shot generation
• Fine-tuned post-training with video-specific RLHF