Generative AI Research Intern - Image-to-Video Diffusion

We are seeking a talented Generative AI Research Intern to work on image-to-video (I2V) diffusion models. This role focuses on building next-generation generative systems that transform still images into high-quality, temporally consistent videos. You will work closely with researchers and engineers to explore cutting-edge diffusion and transformer-based architectures for video generation, motion modeling, and temporal representation learning.

You will: - Lead experimental studies on image-to-video diffusion techniques and temporal generative modeling. - Design, implement, and evaluate model variants for motion modeling and temporal consistency. - Build experimental pipelines and run ablation studies and benchmarking experiments. - Analyze results and summarize findings in internal research reports or presentations. - Study and reproduce recent work in video diffusion and I2V generation. - Participate in research discussions and proposal of new ideas.

Minimum Qualifications

• Currently pursuing a Master’s or Ph.D. degree in Computer Science, Electrical Engineering, Applied Mathematics, or a related field.
• Strong background in computer vision and deep learning, with research or project experience in diffusion models, flow-matching, or transformer-based generative models applied to video generation or spatiotemporal tasks.
• Experience or solid understanding of image-to-video models, video generation, temporal modeling, or video representation.
• Proficiency in Python and PyTorch, with the ability to implement research code independently.
• Familiarity with model training workflows, ablation studies, and experimental result analysis.
• Familiarity with generative video evaluation metrics such as LPIPS, FVD, and perceptual assessment methods.
• Strong motivation for independent research and the ability to drive projects with minimal supervision.
• Ability to read, reproduce, and critically analyze recent research papers.

Preferred Qualifications

• Understanding of video fundamentals such as motion estimation, optical flow, temporal consistency, or alignment techniques.
• Prior experience with large-scale training, multimodal models, or video datasets.
• Publications in top-tier conferences or journals (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, etc.).
• Strong curiosity and motivation for independent research in generative video modeling.