| _version_ | 1866901307673018368 |
|---|---|
| author | Kanna Velusamy |
| author_facet | Kanna Velusamy |
| contents | <p><p><strong>Executive Summary</strong></p><br><p>This technical research report presents a comprehensive comparative analysis of contemporary open-source video face-swapping tools, including <em>Roop</em>, <em>FaceFusion</em>, <em>VisoMaster</em>, and <em>Rope-Pearl</em>. Conducted in December 2025, the study evaluates these tools across critical dimensions: profile view stability, occlusion handling, and architectural limitations.</p></p> <p><p><strong>Key Findings:</strong></p><br><ul><br> <li><strong>The 60&deg; Yaw Limit:</strong> Systematic testing reveals that all current 2D-based methods (using <em>inswapper_128</em>) fail to maintain geometric accuracy when face yaw exceeds 60 degrees.</li><br> <li><strong>The "Ghost Expansion" Artifact:</strong> Profile views consistently exhibit texture bleeding and "face expansion" due to the fundamental lack of depth information in 2D latent spaces.</li><br> <li><strong>Parameter Limits:</strong> The report demonstrates that parameter tuning (masking, detector scores) provides only marginal mitigation and cannot solve the underlying missing-information problem.</li><br></ul></p> <p><p><strong>Research Contribution:</strong></p><br><p>The report identifies the transition from 2D methods to <strong>3D-aware architectures</strong> (such as NeRF and 3D Morphable Models/FLAME) as the necessary research frontier for solving automated face swapping in unconstrained video. This document serves as a benchmark for current tool capabilities and a roadmap for future development.</p></p> <p><p><strong>Target Audience:</strong> Computer Vision Researchers, AI Engineers, Digital Content Creators, and Ethical Hacking Analysts.</p></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18103219 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Comparative Analysis of various video swap tools Kanna Velusamy face swap, Deep Learning, 3D Morphable Models, NeRF, Computer Vision <p><p><strong>Executive Summary</strong></p><br><p>This technical research report presents a comprehensive comparative analysis of contemporary open-source video face-swapping tools, including <em>Roop</em>, <em>FaceFusion</em>, <em>VisoMaster</em>, and <em>Rope-Pearl</em>. Conducted in December 2025, the study evaluates these tools across critical dimensions: profile view stability, occlusion handling, and architectural limitations.</p></p> <p><p><strong>Key Findings:</strong></p><br><ul><br> <li><strong>The 60&deg; Yaw Limit:</strong> Systematic testing reveals that all current 2D-based methods (using <em>inswapper_128</em>) fail to maintain geometric accuracy when face yaw exceeds 60 degrees.</li><br> <li><strong>The "Ghost Expansion" Artifact:</strong> Profile views consistently exhibit texture bleeding and "face expansion" due to the fundamental lack of depth information in 2D latent spaces.</li><br> <li><strong>Parameter Limits:</strong> The report demonstrates that parameter tuning (masking, detector scores) provides only marginal mitigation and cannot solve the underlying missing-information problem.</li><br></ul></p> <p><p><strong>Research Contribution:</strong></p><br><p>The report identifies the transition from 2D methods to <strong>3D-aware architectures</strong> (such as NeRF and 3D Morphable Models/FLAME) as the necessary research frontier for solving automated face swapping in unconstrained video. This document serves as a benchmark for current tool capabilities and a roadmap for future development.</p></p> <p><p><strong>Target Audience:</strong> Computer Vision Researchers, AI Engineers, Digital Content Creators, and Ethical Hacking Analysts.</p></p> |
| title | Comparative Analysis of various video swap tools |
| topic | face swap, Deep Learning, 3D Morphable Models, NeRF, Computer Vision |
| url | https://doi.org/10.5281/zenodo.18103219 |