Text2LIVE: Text-Driven Layered Image and Video Editing (2204.02491v2)

Abstract: We present a method for zero-shot, text-driven appearance manipulation in natural images and videos. Given an input image or video and a target text prompt, our goal is to edit the appearance of existing objects (e.g., object's texture) or augment the scene with visual effects (e.g., smoke, fire) in a semantically meaningful manner. We train a generator using an internal dataset of training examples, extracted from a single input (image or video and target text prompt), while leveraging an external pre-trained CLIP model to establish our losses. Rather than directly generating the edited output, our key idea is to generate an edit layer (color+opacity) that is composited over the original input. This allows us to constrain the generation process and maintain high fidelity to the original input via novel text-driven losses that are applied directly to the edit layer. Our method neither relies on a pre-trained generator nor requires user-provided edit masks. We demonstrate localized, semantic edits on high-resolution natural images and videos across a variety of objects and scenes.

• The paper introduces a text-driven layered editing approach using RGBA layers to achieve localized, high-fidelity modifications.
• It employs Vision-Language models and innovative loss functions to guide semantic edits without pre-trained generators or manual masks.
• The method yields consistent, detailed texture and video edits, outperforming traditional global editing techniques in preserving original content.

Text2LIVE: Text-Driven Layered Image and Video Editing

This paper introduces Text2LIVE, an innovative approach to text-driven image and video editing that enables semantic, localized appearance manipulations. Unlike traditional methods relying on a reference image for style-transfer, this method leverages the capabilities of Vision-LLMs to facilitate flexible and creative edits specified by simple text prompts.

Overview

Text2LIVE employs an edit layer generation mechanism that overlays a real-world image or video with an RGBA layer. This approach maintains high fidelity to the original content while supporting intricate edits, including texture changes and semi-transparent effects, driven solely by text inputs. A notable contribution is the generation of these layers without necessitating a pre-trained generator, user-provided masks, or a global editing process.

The framework capitalizes on the capabilities of the CLIP model to establish loss functions that ensure high-quality edits. Specifically, the generator synthesizes the edit layers by optimizing several losses related to maintaining content fidelity, localized editing, and appearance manipulation to match the target textual description.

Key Contributions

1. Zero-Shot Localization and Fidelity: By generating an RGBA edit layer, Text2LIVE achieves localized editing without auxiliary input guidance and retains original image or video details. The method uses text-based prompts to automatically determine both the target edit and its geographical application within the scene.
2. Internal Dataset Training Approach: The authors utilize an internal dataset derived from the input data. This dataset is augmented textually and visually, enabling the model to learn from rich examples despite starting with a single input instance.
3. Novel Loss Functions: A set of novel loss functions guide the generator to achieve precise and high-quality edits. These include text-based losses applied to both the editing layer and the composite result to tightly integrate text instructions into the generation process.
4. Consistent Video Editing: The work extends to consistent video editing by utilizing layered neural atlases developed from video input. This representation decomposes the video into coherent 2D layers, paving the way for temporally consistent and semantically meaningful edits across frames.

Experimental Results

The paper demonstrates the proficiency of Text2LIVE in applying a wide range of edits on various existing photographs and video scenes. The system surpasses traditional methods by avoiding global transformations, instead opting for local modifications that preserve existing object shapes and spatial hierarchies. Notable examples include altering textures of food items or adding visual effects like smoke or fire to animal images.

Implications and Future Work

This framework advances the utility of text-based editing, allowing for intuitive interaction with digital media content. It represents a shift towards streamlining the editing process by exploiting the synergies between natural language processing and computer vision. On the theoretical front, the approach exemplifies how localized learning from generative models can be combined with robust language understanding to produce detailed and high-quality image alternations.

Future research could focus on expanding the robustness of the editing system to handle more complex scenarios, such as crowded scenes or intricate object interactions. Additionally, jointly optimizing atlas representation with the generative process could further enhance temporal consistency and quality for video inputs.

In conclusion, Text2LIVE presents a robust and adaptable tool for text-driven image and video editing. Its novel methodology significantly enhances local edit application, paving the way for more sophisticated and user-friendly creativity support tools in digital media production.