Hansol Lee

Yunsung Kim, Hansol Lee, Candace Thille et al.

Conventional methods of obtaining student feedback on course experience face a fundamental tradeoff between feedback frequency and quality: as feedback requests become more frequent, participation often declines, and responses become less thoughtful over time. To obtain both timely and thoughtful feedback from students, Kim and Piech (Learning at Scale, 2023) recently proposed a simple, lightweight course feedback mechanism: surveying each student a small number of times per term during randomly selected weeks. Named High-Resolution Course Feedback (HRCF), this method has been shown to elicit feedback that instructors find helpful without imposing excessive burden on students. An important question, however, remains unanswered: is the use of this simple method associated with measurable improvements in students' actual course experiences? We study HRCF use across 103 course offerings, totaling 24,216 student enrollments, over four years from Fall 2021 through Fall 2025, spanning 42 unique computer science courses at an R1 institution. Through a regression analysis of four end-of-term student evaluation items for these courses, we find that first-time use of HRCF is not associated with a measurable change in average student ratings. However, among small- and medium-enrollment (<250 students) course offerings, continued HRCF use is associated with average rating increases of 0.045 to 0.048 points per additional term of use for learning-related items. We observe no statistically significant associations for large-enrollment (250 or more students) course offerings, nor for items measuring instructional quality and course organization. Together, these findings suggest that sustained HRCF use may support improvements in students' learning experiences, but that further design enhancements may be needed to produce measurable improvements in instructional quality and course organization.

Hansol Lee, Yunhoe Ku, Eunseo Kim et al.

Reenacting facial images is an important task that can find numerous applications. We proposed IFaceUV, a fully differentiable pipeline that properly combines 2D and 3D information to conduct the facial reenactment task. The three-dimensional morphable face models (3DMMs) and corresponding UV maps are utilized to intuitively control facial motions and textures, respectively. Two-dimensional techniques based on 2D image warping is further required to compensate for missing components of the 3DMMs such as backgrounds, ear, hair and etc. In our pipeline, we first extract 3DMM parameters and corresponding UV maps from source and target images. Then, initial UV maps are refined by the UV map refinement network and it is rendered to the image with the motion manipulated 3DMM parameters. In parallel, we warp the source image according to the 2D flow field obtained from the 2D warping network. Rendered and warped images are combined in the final editing network to generate the final reenactment image. Additionally, we tested our model for the audio-driven facial reenactment task. Extensive qualitative and quantitative experiments illustrate the remarkable performance of our method compared to other state-of-the-art methods.

Changhwa Lee, Junuk Cha, Hansol Lee et al.

We present HOReeNet, which tackles the novel task of manipulating images involving hands, objects, and their interactions. Especially, we are interested in transferring objects of source images to target images and manipulating 3D hand postures to tightly grasp the transferred objects. Furthermore, the manipulation needs to be reflected in the 2D image space. In our reenactment scenario involving hand-object interactions, 3D reconstruction becomes essential as 3D contact reasoning between hands and objects is required to achieve a tight grasp. At the same time, to obtain high-quality 2D images from 3D space, well-designed 3D-to-2D projection and image refinement are required. Our HOReeNet is the first fully differentiable framework proposed for such a task. On hand-object interaction datasets, we compared our HOReeNet to the conventional image translation algorithms and reenactment algorithm. We demonstrated that our approach could achieved the state-of-the-art on the proposed task.

Andrea Cuadra, Hansol Lee, Jason Cho et al.

People interacting with voice assistants are often frustrated by voice assistants' frequent errors and inability to respond to backchannel cues. We introduce an open-source video dataset of 21 participants' interactions with a voice assistant, and explore the possibility of using this dataset to enable automatic error recognition to inform self-repair. The dataset includes clipped and labeled videos of participants' faces during free-form interactions with the voice assistant from the smart speaker's perspective. To validate our dataset, we emulated a machine learning classifier by asking crowdsourced workers to recognize voice assistant errors from watching soundless video clips of participants' reactions. We found trends suggesting it is possible to determine the voice assistant's performance from a participant's facial reaction alone. This work posits elicited datasets of interactive responses as a key step towards improving error recognition for repair for voice assistants in a wide variety of applications.

Junuk Cha, Hansol Lee, Jaewon Kim et al.

This paper introduces a novel pipeline to reconstruct the geometry of interacting multi-person in clothing on a globally coherent scene space from a single image. The main challenge arises from the occlusion: a part of a human body is not visible from a single view due to the occlusion by others or the self, which introduces missing geometry and physical implausibility (e.g., penetration). We overcome this challenge by utilizing two human priors for complete 3D geometry and surface contacts. For the geometry prior, an encoder learns to regress the image of a person with missing body parts to the latent vectors; a decoder decodes these vectors to produce 3D features of the associated geometry; and an implicit network combines these features with a surface normal map to reconstruct a complete and detailed 3D humans. For the contact prior, we develop an image-space contact detector that outputs a probability distribution of surface contacts between people in 3D. We use these priors to globally refine the body poses, enabling the penetration-free and accurate reconstruction of interacting multi-person in clothing on the scene space. The results demonstrate that our method is complete, globally coherent, and physically plausible compared to existing methods.