Human Pose Manipulation and Novel View Synthesis using Differentiable Rendering (2111.12731v2)

Abstract: We present a new approach for synthesizing novel views of people in new poses. Our novel differentiable renderer enables the synthesis of highly realistic images from any viewpoint. Rather than operating over mesh-based structures, our renderer makes use of diffuse Gaussian primitives that directly represent the underlying skeletal structure of a human. Rendering these primitives gives results in a high-dimensional latent image, which is then transformed into an RGB image by a decoder network. The formulation gives rise to a fully differentiable framework that can be trained end-to-end. We demonstrate the effectiveness of our approach to image reconstruction on both the Human3.6M and Panoptic Studio datasets. We show how our approach can be used for motion transfer between individuals; novel view synthesis of individuals captured from just a single camera; to synthesize individuals from any virtual viewpoint; and to re-render people in novel poses. Code and video results are available at https://github.com/GuillaumeRochette/HumanViewSynthesis.

• The paper introduces a differentiable rendering framework using diffuse Gaussian primitives for human pose manipulation and novel view synthesis from single images.
• Evaluation on Human3.6M and Panoptic Studio shows strong results in image reconstruction, motion transfer, and virtual view synthesis.
• This approach has significant implications for applications like VR, film, and telepresence by enabling realistic human rendering and synthesis from single images.

Insights on Human Pose Manipulation and Novel View Synthesis Using Differentiable Rendering

The paper presents a sophisticated method for achieving human pose manipulation and novel view synthesis by employing a differentiable rendering approach. This technique stands out from traditional mesh-based systems through its innovative use of diffuse Gaussian primitives to directly represent the underlying skeletal structure. The method promises significant advancements in rendering novel views of individuals captured from single cameras, synthesizing appearances from virtual viewpoints, and enabling motion transfer between different subjects.

Technical Contributions

The primary contribution of the work is the development of a unique differentiable rendering framework that simplifies the optimization process. Instead of relying on complex mesh representations, the framework adopts a representation using semantically meaningful diffuse Gaussian primitives. This novel choice enables a high level of disentanglement between pose and appearance, facilitating robust optimization and efficient rendering. The framework is structured as follows:

1. Pose and Appearance Extraction: The method first infers the 3D pose and extracts appearance features from a given image using a 2D detector. The human skeleton is represented as a graph of joints and connections, with appearance captured for each segment.
2. Differentiable Rendering: A novel renderer computes a high-dimensional latent image using the extracted pose and appearance information. This step involves rendering the Gaussian primitives using a realistic camera model to create a high-fidelity image from various viewpoints.
3. Image Synthesis: The final image is generated by converting the latent representation into an output image using an encoder-decoder architecture. This approach allows the system to produce highly realistic images through end-to-end training, leveraging multi-view data.

Experimental Results

The methodology was evaluated on the Human3.6M and Panoptic Studio datasets, demonstrating the effectiveness of the approach through strong numerical results. The system was shown to excel in several tasks including:

• Image Reconstruction: The evaluation indicated that the synthesized images closely matched target images, as measured by metrics like LPIPS, PSNR, and SSIM. Models trained with perceptual and adversarial losses exhibited superior detail and high-frequency fidelity.
• Motion Transfer: The framework adeptly performed motion transfer, synthesizing a novel view by blending pose and appearance from different subjects, further validating the disentanglement of pose and appearance.
• Virtual View Synthesis: The system was capable of generating virtual camera perspectives that did not exist in the original dataset, showcasing its ability to extrapolate human figures into unseen views.

Implications and Future Directions

This research has significant implications for the domains of computer vision and graphics, particularly in scenarios where realistic rendering of human figures is critical, such as in virtual reality, film, and interactive gaming. The framework's ability to synthesize from a single image suggests potential applications in telepresence and remote work environments where camera setups are constrained.

On a theoretical level, the representation of humans using Gaussian primitives in high-dimensional space advances the understanding of disentangling appearance and geometry. This work could influence future research on interpretable machine learning models that integrate physical constraints and semantic understanding.

Looking forward, potential developments could include expanding this approach to dynamic scenes or generalizing beyond human figures to animate creatures or articulated objects in real-time applications. Further exploration of integrating this framework with other emerging technologies like neural radiance fields (NeRF) might offer even greater flexibility and realism in novel view synthesis tasks.

Overall, the paper lays a foundation for continued exploration into differentiable graphics and the elegant integration of machine learning with classical rendering concepts. The approach's scalability and adaptability mark important steps towards realizing automated, high-quality image synthesis in diverse applications.