Nvidia corporation (20240253217). LOSS-GUIDED DIFFUSION MODELS simplified abstract
Contents
LOSS-GUIDED DIFFUSION MODELS
Organization Name
Inventor(s)
Arash Vahdat of Mountain View CA (US)
Hongxu Yin of San Jose CA (US)
Jan Kautz of Lexington MA (US)
Jiaming Song of San Carlos CA (US)
Ming-Yu Liu of San Jose CA (US)
Morteza Mardani of Santa Clara CA (US)
Qinsheng Zhang of Atlanta GA (US)
LOSS-GUIDED DIFFUSION MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240253217 titled 'LOSS-GUIDED DIFFUSION MODELS
The patent application describes apparatuses, systems, and techniques to calculate a combined loss value by applying one or more loss functions to a plurality of samples generated by a diffusion model. This process updates the samples to determine synthesized motions of one or more objects.
- The innovation involves calculating a combined loss value using loss functions applied to samples from a diffusion model.
- The technology updates the samples to determine synthesized motions of objects.
- The system utilizes multiple loss functions to enhance accuracy in motion synthesis.
- By applying this method, the technology can predict and simulate complex motions effectively.
- The innovation can be applied in various fields such as robotics, animation, and virtual reality.
Potential Applications: - Robotics: Enhancing motion prediction and control in robotic systems. - Animation: Improving the realism and fluidity of animated movements. - Virtual Reality: Creating more immersive and realistic virtual environments.
Problems Solved: - Enhances accuracy in predicting and synthesizing complex motions. - Improves the efficiency of motion simulation processes. - Enables more realistic and natural movements in various applications.
Benefits: - Increased accuracy in motion prediction and synthesis. - Enhanced realism and fluidity in animated and virtual environments. - Improved control and efficiency in robotic systems.
Commercial Applications: Title: Motion Prediction and Synthesis Technology for Robotics and Animation This technology can be utilized in industries such as robotics, animation studios, and virtual reality development companies. It can improve the quality and efficiency of motion prediction and synthesis processes, leading to more realistic and engaging products in these sectors.
Questions about Motion Prediction and Synthesis Technology: 1. How does the technology improve motion prediction accuracy? The technology enhances accuracy by applying multiple loss functions to samples from a diffusion model, updating them to determine synthesized motions effectively. 2. What are the potential applications of this technology in virtual reality? This technology can be used to create more immersive and realistic virtual environments by improving the accuracy and fluidity of motion synthesis.
Original Abstract Submitted
apparatuses, systems, and techniques to calculate a combined loss value based on applying one or more loss functions to the plurality of samples generated by a diffusion model to update the samples to determine a synthesized motions of one or more objects.
