GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis (2312.02155v3)

Abstract: We present a new approach, termed GPS-Gaussian, for synthesizing novel views of a character in a real-time manner. The proposed method enables 2K-resolution rendering under a sparse-view camera setting. Unlike the original Gaussian Splatting or neural implicit rendering methods that necessitate per-subject optimizations, we introduce Gaussian parameter maps defined on the source views and regress directly Gaussian Splatting properties for instant novel view synthesis without any fine-tuning or optimization. To this end, we train our Gaussian parameter regression module on a large amount of human scan data, jointly with a depth estimation module to lift 2D parameter maps to 3D space. The proposed framework is fully differentiable and experiments on several datasets demonstrate that our method outperforms state-of-the-art methods while achieving an exceeding rendering speed.

• The paper introduces a novel framework that predicts pixel-wise 3D Gaussian parameters to synthesize novel human views without subject-specific optimizations.
• It jointly trains depth estimation with Gaussian regression to ensure precise alignment between 2D images and 3D representations.
• The method achieves real-time 2K rendering at over 25 fps, outperforming existing techniques in both quality and speed.

Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis

The paper entitled "GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis," presents a new methodology for synthesizing human novel views using a 3D Gaussian splatting approach. This technique addresses the task's challenges by developing a method that operates in real-time, maintaining high fidelity and resolution in generated images despite limited camera views.

The authors introduce GPS-Gaussian, a framework utilizing 3D Gaussian Splatting for rendering novel views. Previous methods in view synthesis often relied on computationally expensive per-subject optimizations, whereas this approach aims to achieve generalization across different human subjects without such overheads. The method involves pixel-wise prediction of Gaussian parameters directly on 2D image planes and subsequently lifts this information to 3D for rendering novel viewpoints.

Several innovative contributions in this paper are worth highlighting:

1. Generalizable 3D Gaussian Splatting:
   • The technique predicts Gaussian parameter maps (position, color, scaling, rotation, and opacity) for each pixel of the source images, allowing 3D Gaussians to be formed without prior subject-specific optimizations. This is achieved using a combination of depth and Gaussian parameter regression, marking a significant improvement over previously established methods.
2. Joint Training of Depth and Gaussian Parameters:
   • The framework integrates an iterative depth estimation module capable of working in tandem with a Gaussian parameter regression module. Through joint training, the proposed model ensures consistent alignment between 2D and 3D space representations.
3. Real-time Performance:
   • The practical implications of rendering 2K-resolution images at over 25 frames per second on a standard GPU platform highlight the system’s efficiency. The fast rendering speed does not sacrifice image quality, outperforming state-of-the-art methods ENeRF, FloRen, and 3D-GS in experimental datasets.

Experiments demonstrate a superior trade-off between speed and quality compared to existing methods, with notable improvements in PSNR, SSIM, and LPIPS metrics across various datasets. The relevance of this work extends into fields requiring efficient and accurate human NVS, such as virtual reality, augmented reality, and real-time immersive media applications.

Looking forward, this approach may open doors for further exploration in AI-based image and video synthesis, beyond human performers, potentially adapting the robust generalization capabilities to diverse and complex environments. However, challenges remain in adapting GPS-Gaussian to more general cases beyond controlled human subject settings, such as dynamic lighting conditions or complex background compositions. Addressing these limitations would significantly broaden the applicability of this method.

In summary, this paper presents a significant advance in the field of real-time view synthesis, introducing a novel way to generalize across subjects while maintaining computational efficiency. The real-time, high-quality rendering achieved by GPS-Gaussian sets a promising benchmark for future developments in rendering technologies, presenting an exciting opportunity for further exploration of 3D Gaussian splatting methods in AI.