FACSIMILE: Fast and Accurate Scans From an Image in Less Than a Second
This enables easier creation of virtual human representations, addressing a domain-specific need in computer vision and graphics.
The paper tackles the problem of detailed body shape estimation from a single photo, achieving fast and accurate results with a method that recovers geometry at the original image resolution without depth supervision.
Current methods for body shape estimation either lack detail or require many images. They are usually architecturally complex and computationally expensive. We propose FACSIMILE (FAX), a method that estimates a detailed body from a single photo, lowering the bar for creating virtual representations of humans. Our approach is easy to implement and fast to execute, making it easily deployable. FAX uses an image-translation network which recovers geometry at the original resolution of the image. Counterintuitively, the main loss which drives FAX is on per-pixel surface normals instead of per-pixel depth, making it possible to estimate detailed body geometry without any depth supervision. We evaluate our approach both qualitatively and quantitatively, and compare with a state-of-the-art method.