Generative AI Video Pose and Environment Replacement
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Solution Overview
Problem
AI-based video and image generation technologies face challenges in maintaining video and image quality and avoiding unwanted artifacts, particularly in the accurate manipulation of body parts, leading to distorted or misaligned features during generation or transformation processes.
Innovation Solution
A method and system using generative AI that involves encoding images and text into embeddings, applying noise and spatial transformations, and iteratively refining these transformations to generate a realistic output image with the person in a new pose and environment, while preserving their identity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI-based video and image generation technologies are used to transform and enhance content, then the creativity and versatility of the generated content are improved, but video and image quality deteriorate and unwanted artifacts emerge
Solution Approach 1:
The patent segments the image generation process into multiple independent components: a base model for general image generation, a pose guidance module for controlling body posture, and a face restoration module for preserving facial details. This segmentation allows each component to specialize in specific tasks, improving overall image quality while maintaining creative versatility.
Solution Approach 2:
The patent introduces intermediate representations and guidance mechanisms that mediate between the creative generation process and quality control. Pose embeddings and face feature embeddings act as intermediaries that guide the generation process without directly constraining it, allowing high-quality output with preserved details.
2Adaptability or versatility
If AI models manipulate video and image data to change scene or environment, then the adaptability of the content is improved, but body parts become distorted or misaligned
Solution Approach 1:
The patent applies preliminary pose guidance before the main generation process. By providing pose embeddings that encode desired body configurations in advance, the model can generate images with correct body part alignment from the outset, preventing distortions rather than correcting them later.
Solution Approach 2:
The patent applies different quality control mechanisms to different regions of the image. Face restoration is applied specifically to facial regions to preserve identity and details, while pose guidance is applied to body regions to maintain anatomical correctness. This localized approach ensures high precision where needed without compromising overall creative freedom.
3Adaptability or versatility
If AI-driven filters and effects are applied to modify user-generated content, then the versatility of content creation is improved, but the perceived quality and realism of the videos and images deteriorate
Solution Approach 1:
The patent incorporates feedback mechanisms where the generated images are evaluated against pose constraints and face similarity metrics. This feedback loop ensures that creative transformations maintain anatomical correctness and preserve identity, enhancing perceived realism while retaining versatility.
Solution Approach 2:
The patent controls key parameters such as pose embeddings and face feature embeddings to maintain realism. By carefully adjusting these parameters during generation, the system can apply creative effects while preserving the natural appearance and realism of the subject.
Data Source
AI summary
Systems and methods for replacement of scene, pose, and environment in videos and images using generative AI are provided. An example method includes receiving a first image of a face of a person, a second image of a body adopting a pose, and a text including a description of an environment of a scene; encoding the first image into an image embedding; extracting, from the second image, information concerning the pose of the body; encoding the text into a text embedding; randomly generating a noise for the image embedding; combining the noise and the image embedding to obtain a noisy image embedding; providing the noisy image embedding, the text embedding, and the information concerning the pose to a neural network to obtain a second noise; removing the second noise from the noisy image embedding to obtain a denoised image embedding; and decoding the denoised image embedding into an output image.