On Mitigating Stability-Plasticity Dilemma in CLIP-guided Image Morphing via Geodesic Distillation Loss

Abstract

Large-scale language-vision pre-training models, such as CLIP, have achieved remarkable results in text-guided image morphing by leveraging several unconditional generative models. However, existing CLIP-guided methods face challenges in achieving photorealistic morphing when adapting the generator from the source to the target domain. Specifically, current guidance methods fail to provide detailed explanations of the morphing regions within the image, leading to misguidance and catastrophic forgetting of the original image’s fidelity. In this paper, we propose a novel approach considering proper regularization losses to overcome these difficulties by addressing the SP dilemma in CLIP guidance. Our approach consists of two key components: (1) a geodesic cosine similarity loss that minimizes inter-modality features (i.e., image and text) in a projected subspace of CLIP space, and (2) a latent regularization loss that minimizes intra-modality features (i.e., image and image) on the image manifold. By replacing the naive directional CLIP loss in a drop-in replacement manner, our method achieves superior morphing results for both images and videos across various benchmarks, including CLIP-inversion.