1. Design a multimodal controllable generation framework supporting various inputs such as keyboard, controller, trajectory, and voice.
2. Research high-fidelity generation under sparse control signals and explore the extension of conditional control paradigms in the video domain.
3. Achieve the decoupling and combination of trajectory control, camera control, motion control, and posture control.
4. Design dedicated control modules tailored to different game genres.
5. Develop video generation capabilities for multi-player collaborative control, ensuring low-latency response and inter-frame state retention.
6. Collaborate with the Engine/Agent teams to define standardized interfaces for controllable signals.
7. Drive the joint optimization of controllable generation and Reinforcement Learning post-training.
1. Ph.D. in AI-related fields, focusing on Computer Vision/Deep Learning.
2. Proficient in conditional generation models and multi-player/multi-channel control technologies; familiar with the principles of diffusion models.
3. Project experience in camera control, trajectory control, or posture control.
4. Understanding of game engine input/output logic and frame synchronization mechanisms.
5. Proficient in Python/PyTorch, with publications in controllable video generation or conditional generation.
6. Experience in Game AI or game development is preferred.
7. Experience in real-time/interactive video generation projects is preferred.
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