SynthForge: Synthesizing High-Quality Face Dataset with Controllable 3D Generative Models
This work addresses the challenge of generating high-quality, annotated synthetic data for computer vision tasks, but it is incremental as it builds on existing generative models.
The paper tackled the problem of using controllable 3D generative models to create synthetic face datasets with consistent annotations for downstream tasks, achieving competitive performance against state-of-the-art models using only synthetic data.
Recent advancements in generative models have unlocked the capabilities to render photo-realistic data in a controllable fashion. Trained on the real data, these generative models are capable of producing realistic samples with minimal to no domain gap, as compared to the traditional graphics rendering. However, using the data generated using such models for training downstream tasks remains under-explored, mainly due to the lack of 3D consistent annotations. Moreover, controllable generative models are learned from massive data and their latent space is often too vast to obtain meaningful sample distributions for downstream task with limited generation. To overcome these challenges, we extract 3D consistent annotations from an existing controllable generative model, making the data useful for downstream tasks. Our experiments show competitive performance against state-of-the-art models using only generated synthetic data, demonstrating potential for solving downstream tasks. Project page: https://synth-forge.github.io