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Supplementary Materials for SIAvatar: Animatable 3D Gaussian Avatar from a Single Image

Test Submission

Please refer to our supplementary materials as follows:

The supplementary_AniMake PDF includes additional results, and detailed explanation.

The demo video provides a demonstration along with sample results.

Facial Expression Recognition (FER) has achieved significant success in recent years due to the rise of deep learning. Meanwhile, latent semantic information is crucial for recognizing facial expressions with subtle differences. Inspired by inconsistencies in learning intensity across different layers of deep learning networks — where shallow-layer features lack generalization and task relevance compared to deep-layer features — we propose a novel Hierarchical Semantic Transfer (HST) method.

Facial Expression Recognition (FER) has achieved significant success in recent years due to the rise of deep learning. Meanwhile, latent semantic information is crucial for recognizing facial expressions with subtle differences. Inspired by inconsistencies in learning intensity across different layers of deep learning networks — where shallow-layer features lack generalization and task relevance compared to deep-layer features — we propose a novel Hierarchical Semantic Transfer (HST) method.

Supplementary of IMPROVING OPEN-WORLD CLASS-AGNOSTIC OBJECT DETECTORS VIA FEATURE DISTILLATION WITH STUDENT-AWARE ADAPTATION

Supplementary materials of GGMix for ICIP2025

SUPPLEMENTARY MATERIAL

SUPPLEMENTARY MATERIAL

The rise of generative models has transformed image generation and editing, enabling high-quality, user-guided outputs. Iterative face editing, essential for applications like virtual makeup and entertainment, allows users to refine images progressively. However, this process often leads to artifact accumulation, semantic inconsistency, and quality degradation over multiple edits. Existing methods, while effective in single-step modifications, struggle with sequential edits.