Detailed Human Avatars from Monocular Video (1808.01338v1)

Abstract: We present a novel method for high detail-preserving human avatar creation from monocular video. A parameterized body model is refined and optimized to maximally resemble subjects from a video showing them from all sides. Our avatars feature a natural face, hairstyle, clothes with garment wrinkles, and high-resolution texture. Our paper contributes facial landmark and shading-based human body shape refinement, a semantic texture prior, and a novel texture stitching strategy, resulting in the most sophisticated-looking human avatars obtained from a single video to date. Numerous results show the robustness and versatility of our method. A user study illustrates its superiority over the state-of-the-art in terms of identity preservation, level of detail, realism, and overall user preference.

• The paper proposes a novel approach that leverages 2D facial landmark integration to enhance 3D facial accuracy.
• It employs shading-based refinement and efficient texture stitching to capture intricate garment details and facial features.
• User studies indicate that the method outperforms state-of-the-art techniques, preserving individual identity with high realism.

Detailed Human Avatars from Monocular Video: An Overview

The paper "Detailed Human Avatars from Monocular Video" by Alldieck et al. presents a significant advancement in the field of computer-generated avatars, focusing on the precise reconstruction of human features using only monocular video input. The research utilizes parameterized body models, enhanced by facial landmarks, shading-based refinement, and an innovative texture synthesis approach, aiming to generate highly detailed avatars that preserve individual identity characteristics.

Methodology

The proposed method extends existing techniques by integrating facial landmark detection and shape-from-shading computations into the body modeling process. It builds on the SMPL model framework, initially estimating a medium-resolution avatar which is consequently refined by capturing facial features and garment details. Key innovations include:

1. Facial Landmarks Integration: The incorporation of 2D facial landmarks enhances the three-dimensional facial accuracy of the model, enabling a more precise reconstruction of the human face.
2. Illumination and Shape-from-Shading: By analyzing shading cues across frames, the model synthesizes high-frequency details like garment wrinkles and facial features, significantly enriching the detail quality of avatars.
3. Efficient Texture Stitching: A graph cut-based method is employed for seamless texture integration from multiple views, ensuring high-fidelity texturing while minimizing artifacts such as color spilling. The semantic texture prior further improves this process by preventing misalignment issues in texture blending.
4. Semantic Texture Prior: This component uses a semantic texture prior to inform the generation of the final texture map, reducing potential errors where colors and textures are incorrectly applied across different surface areas of the avatar.

Results and Implications

Through robust experimental evaluation and a user paper, the paper demonstrates that the proposed approach outperforms previous state-of-the-art methodologies in preserving identity and achieving a higher realism in avatars. Users preferred the enhanced avatars produced by this method due to their realistic texture and identity retention. Specifically, the avatars generated were favored for detail and realism in approximately 90% of user comparisons.

The implications of this work are broad, spanning practical applications in virtual reality (VR), augmented reality (AR), entertainment, telestration, and even potential biometric security solutions. The ability to produce personalized avatars with high levels of detail from minimal input opens new possibilities for scalable avatar generation systems.

Future Directions

While the paper showcases significant improvements, future work could address scenarios involving more complex clothing geometry, such as skirts and coats, which present additional challenges due to their non-body-conforming nature. Moreover, the authors suggest exploration into reconstructing avatars from video footage captured in uncontrolled environments, expanding the utility of their approach in real-world applications.

In conclusion, the work of Alldieck et al. represents a notable contribution to the domain of avatar creation, emphasizing the importance of detail in preserving identity and enhancing the visual realism of computer-generated human models. As advances in this field continue, the integration of such sophisticated avatar modeling into consumer applications appears increasingly feasible.