San Jose, CA, United States of America

Yangtuanfeng Wang

This inventor holds 1 USPTO granted patent. Top assignee: Adobe, Inc.. Active years: 2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Yangtuanfeng Wang. Retrieved from https://idiyas.com/inventor/yangtuanfeng-wang-dmlwtkyr

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

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Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: Yangtuanfeng Wang and 4D-Guided Video Generation

Introduction

Yangtuanfeng Wang is an inventor based in San Jose, California, and is associated with Adobe, Inc. His patent work addresses the use of diffusion models to generate four-dimensional video guided by prompts and 3D mesh data.

Latest Patents

Wang is named on 1 patent: “Systems and Techniques to Perform 4D-Guided Video Generation with Diffusion Models.” The disclosed techniques receive a prompt and an input mesh, then generate keyframes using depth and UV coordinate maps. Features extracted from those keyframes are used with the prompt in a diffusion model to generate video frames. The approach also describes UV-guided noise initialization for individual objects and feature injection during regeneration.

Career Highlights

Wang’s listed patent reflects work at the intersection of generative AI, 3D representations, and video synthesis. The invention describes a process for using mesh-derived information and keyframe features to guide the generation of temporally structured video.

Collaborations

Wang’s listed coworker is Shengqu Cai. The patent information provided connects their work through the disclosed 4D-guided video-generation technology.

Conclusion

Yangtuanfeng Wang’s patent highlights a method for combining prompts, mesh data, keyframes, and diffusion models to guide 4D video generation.

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
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