Robot Shape and Location Retention in Video Generation Using Diffusion Models

Diffusion models have marked a significant mile-stone in the enhancement of image and video generation technologies. However, generating videos that precisely retain the shape and location of moving objects such as robots remains a challenge. This paper presents diffusion models specifically tailored to generate videos that accurately maintain the shape and location of mobile robots. The proposed models incorporate techniques such as embedding accessible robot pose information and applying semantic mask regulation within the scalable and efficient ConvNext backbone network. These techniques are designed to refine intermediate outputs, therefore improving the retention performance of shape and location. Through extensive experimentation, our models have demonstrated notable improvements in maintaining the shape and location of different robots, as well as enhancing overall video generation quality, compared to the benchmark diffusion model. Codes will be open-sourced at: https://github.com/PengPaulWang/diffusion-robots.

Authors

• Peng Wang 0076
• Zhihao Guo
• Abdul Latheef Sait
• Minh Huy Pham

Keywords

• Training
• Accuracy
• Shape
• Shape measurement
• Semantics
• Diffusion models
• Regulation
• Mobile robots
• Robots
• Intelligent robots
• Diffusion Model
• Shape Retention
• Video Generation
• Robot Shape
• Image Generation
• Backbone Network
• Pose Information
• Robot Pose
• Local Information
• Gaussian Noise
• Diffusion Process
• Intersection Over Union
• Reversible Process
• Kullback-Leibler
• Noisy Data
• Object Location
• Peak Signal-to-noise Ratio
• Ethical Challenges
• Object Shape
• Benchmark Model
• Forward Process
• Robot Localization
• Human-robot Collaboration
• Robot Operating System
• Data For Model Training
• Original Frame
• Legal Challenges
• Types Of Robots
• Peak Signal-to-noise Ratio Values
• Model Performance