TIFu: Tri-directional Implicit Function for High-Fidelity 3D Character
  Reconstruction
Byoungsung Lim Seong-Whan Lee
Recent advances in implicit function-based approaches have shown promising
results in 3D human reconstruction from a single RGB image. However, these
methods are not sufficient to extend to more general cases, often generating
dragged or disconnected body parts, particularly for animated characters. We
argue that these limitations stem from the use of the existing point-level 3D
shape representation, which lacks holistic 3D context understanding.
Voxel-based reconstruction methods are more suitable for capturing the entire
3D space at once, however, these methods are not practical for high-resolution
reconstructions due to their excessive memory usage. To address these
challenges, we introduce Tri-directional Implicit Function (TIFu), which is a
vector-level representation that increases global 3D consistencies while
significantly reducing memory usage compared to voxel representations. We also
introduce a new algorithm in 3D reconstruction at an arbitrary resolution by
aggregating vectors along three orthogonal axes, resolving inherent problems
with regressing fixed dimension of vectors. Our approach achieves
state-of-the-art performances in both our self-curated character dataset and
the benchmark 3D human dataset. We provide both quantitative and qualitative
analyses to support our findings.