Student Researcher [Seed Vision - Long-Range Video Generation] - 2026 Start (PhD)

Job Description:

About the team The Seed Vision Team focuses on foundational models for visual generation, developing multimodal generative models, and carrying out leading research and application development to solve fundamental computer vision challenges in GenAI. Researching and developing foundational models for visual generation (images and videos), ensuring high interactivity and controllability in visual generation, understanding patterns in videos, and exploring various visual-oriented tasks based on generative foundational models. PhD internships at ByteDance provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and development events, and collaboration with industry experts. Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date). Responsibilities - Develop scalable architectures for long-range video generation with consistent motion, identity, and layout. - Explore hierarchical or recurrent latent structures to support generation across long temporal spans. - Address challenges in temporal drift, motion collapse, and high-frequency detail retention. - Investigate autoregressive or chunked generation strategies that balance quality and memory. - Design evaluation protocols for long video quality (e.g., realism, consistency, semantic continuity)

Candidate Requirements:

Minimum Qualifications: - Currently pursuing a PhD in Computer Vision, Machine Learning, or a related field. - Research experience in generative modeling, especially for video, motion, or temporal sequences. - First-author publications in CVPR, ICCV, ECCV, NeurIPS, ICLR, or ICML. - Proficiency in deep learning frameworks and experience with large-scale video datasets. Preferred Qualifications: - Experience with diffusion or transformer-based video models, or long-context sequence generation. - Familiarity with long-form video datasets. - Understanding of perceptual metrics and user-study-based video evaluation