A five-stage pipeline using Roop face-swapping anonymizes pedestrians in Egyptian street images while preserving gaze and expression cues for AV intention models.
Ghost 2.0: generative high-fidelity one shot transfer of heads
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
While the task of face swapping has recently gained attention in the research community, a related problem of head swapping remains largely unexplored. In addition to skin color transfer, head swap poses extra challenges, such as the need to preserve structural information of the whole head during synthesis and inpaint gaps between swapped head and background. In this paper, we address these concerns with GHOST 2.0, which consists of two problem-specific modules. First, we introduce enhanced Aligner model for head reenactment, which preserves identity information at multiple scales and is robust to extreme pose variations. Secondly, we use a Blender module that seamlessly integrates the reenacted head into the target background by transferring skin color and inpainting mismatched regions. Both modules outperform the baselines on the corresponding tasks, allowing to achieve state of the art results in head swapping. We also tackle complex cases, such as large difference in hair styles of source and target. Code is available at https://github.com/ai-forever/ghost-2.0
fields
cs.CV 2years
2026 2representative citing papers
Adaptive Head Synthesis (AHS) employs head-reenacted synthetic data augmentation to enable robust head swapping on full upper-body images without paired training data.
citing papers explorer
-
Swapping Faces, Saving Features: A Dual-Purpose Pipeline for Pedestrian Privacy in ITS
A five-stage pipeline using Roop face-swapping anonymizes pedestrians in Egyptian street images while preserving gaze and expression cues for AV intention models.
-
AHS: Adaptive Head Synthesis via Synthetic Data Augmentations
Adaptive Head Synthesis (AHS) employs head-reenacted synthetic data augmentation to enable robust head swapping on full upper-body images without paired training data.