Sketch-based skeleton-driven 2D animation and motion capture.

This research is concerned with the development of a set of novel sketch-based skeleton-driven 2D animation techniques, which allow the user to produce realistic 2D character animation efficiently. The technique consists of three parts: sketch-based skeleton-driven 2D animation production, 2D motion capture and a cartoon animation filter. For 2D animation production, the traditional way is drawing the key-frames by experienced animators manually. It is a laborious and time-consuming process. With the proposed techniques, the user only inputs one image ofa character and sketches a skeleton for each subsequent key-frame. The system then deforms the character according to the sketches and produces animation automatically. To perform 2D shape deformation, a variable-length needle model is developed, which divides the deformation into two stages: skeleton driven deformation and nonlinear deformation in joint areas. This approach preserves the local geometric features and global area during animation. Compared with existing 2D shape deformation algorithms, it reduces the computation complexity while still yielding plausible deformation results. To capture the motion of a character from exiting 2D image sequences, a 2D motion capture technique is presented. Since this technique is skeleton-driven, the motion of a 2D character is captured by tracking the joint positions. Using both geometric and visual features, this problem can be solved by ptimization, which prevents self-occlusion and feature disappearance. After tracking, the motion data are retargeted to a new character using the deformation algorithm proposed in the first part. This facilitates the reuse of the characteristics of motion contained in existing moving images, making the process of cartoon generation easy for artists and novices alike. Subsequent to the 2D animation production and motion capture,"Cartoon Animation Filter" is implemented and applied. Following the animation principles, this filter processes two types of cartoon input: a single frame of a cartoon character and motion capture data from an image sequence. It adds anticipation and follow-through to the motion with related squash and stretch effect

Pemanfaatan Gambar Sequence Sebagai Referensi Dalam Pembuatan Animasi Karakter Kartun 2d Guna Memenuhi Standar 12 Prinsip Animasi

12 principles of animation is the rule in making an animated character to make it look alive. To fulfill the required reference in the manufacturing process. This study will explore the use of video as a medium of reference in the process of making animation. The video will be used as the image sequence and the image will later be used as a reference in drawing animation. This technique is expected to help an animator to create animations with a standard 12 principles of animation with sufficient time

Facial Expression Retargeting from Human to Avatar Made Easy

Facial expression retargeting from humans to virtual characters is a useful technique in computer graphics and animation. Traditional methods use markers or blendshapes to construct a mapping between the human and avatar faces. However, these approaches require a tedious 3D modeling process, and the performance relies on the modelers' experience. In this paper, we propose a brand-new solution to this cross-domain expression transfer problem via nonlinear expression embedding and expression domain translation. We first build low-dimensional latent spaces for the human and avatar facial expressions with variational autoencoder. Then we construct correspondences between the two latent spaces guided by geometric and perceptual constraints. Specifically, we design geometric correspondences to reflect geometric matching and utilize a triplet data structure to express users' perceptual preference of avatar expressions. A user-friendly method is proposed to automatically generate triplets for a system allowing users to easily and efficiently annotate the correspondences. Using both geometric and perceptual correspondences, we trained a network for expression domain translation from human to avatar. Extensive experimental results and user studies demonstrate that even nonprofessional users can apply our method to generate high-quality facial expression retargeting results with less time and effort.Comment: IEEE Transactions on Visualization and Computer Graphics (TVCG), to appea

PEMBUATAN ANIMASI DENGAN METODE STOP MOTION SEBAGAI REFERENSI RANCANGAN GAMBAR SEQUENCE

Dalam membuat animasi agar terlihat hidup memiliki 12 aturan prinsip animasi. Agar dapat referensi tersebut terpenuhi yang harus dilakukan dalam proses manufaktur pembuatan animasi. Penelitian ini akan mengeksplorasi penggunaan karakter dari action figure atau berupa patung kemudian mengatur posisi karakter, kemudian di ambil gambar pada setiap gerakan yang di gabungkan kemudian di olah menjadi video. Video kemudian di manipulasi untuk mendapatkan gambar yang hidup yang dapat digunakan sebagai referensi dalam pembuatan film animasi. Metode yang digunakan dalah stop motion yaitu mengambil gambar untuk dijadikan frame paling sedikit 12 sampai 24 gambar untuk menjadi sebuah video. Harapan yang di dapat untuk membantu memudahkan animator dalam membuat animasi dengan memenuhi standar 12 prinsip animasi dengan maksimal

Drawing from motion capture : developing visual languages of animation

The work presented in this thesis aims to explore novel approaches of combining motion capture with drawing and 3D animation. As the art form of animation matures, possibilities of hybrid techniques become more feasible, and crosses between traditional and digital media provide new opportunities for artistic expression. 3D computer animation is used for its keyframing and rendering advancements, that result in complex pipelines where different areas of technical and artistic specialists contribute to the end result. Motion capture is mostly used for realistic animation, more often than not for live-action filmmaking, as a visual effect. Realistic animated films depend on retargeting techniques, designed to preserve actors performances with a high degree of accuracy. In this thesis, we investigate alternative production methods that do not depend on retargeting, and provide animators with greater options for experimentation and expressivity. As motion capture data is a great source for naturalistic movements, we aim to combine it with interactive methods such as digital sculpting and 3D drawing. As drawing is predominately used in preproduction, in both the case of realistic animation and visual effects, we embed it instead to alternative production methods, where artists can benefit from improvisation and expression, while emerging in a three-dimensional environment. Additionally, we apply these alternative methods for the visual development of animation, where they become relevant for the creation of specific visual languages that can be used to articulate concrete ideas for storytelling in animation

Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes

Humans have long been recorded in a variety of forms since antiquity. For example, sculptures and paintings were the primary media for depicting human beings before the invention of cameras. However, most current human-centric computer vision tasks like human pose estimation and human image generation focus exclusively on natural images in the real world. Artificial humans, such as those in sculptures, paintings, and cartoons, are commonly neglected, making existing models fail in these scenarios. As an abstraction of life, art incorporates humans in both natural and artificial scenes. We take advantage of it and introduce the Human-Art dataset to bridge related tasks in natural and artificial scenarios. Specifically, Human-Art contains 50k high-quality images with over 123k person instances from 5 natural and 15 artificial scenarios, which are annotated with bounding boxes, keypoints, self-contact points, and text information for humans represented in both 2D and 3D. It is, therefore, comprehensive and versatile for various downstream tasks. We also provide a rich set of baseline results and detailed analyses for related tasks, including human detection, 2D and 3D human pose estimation, image generation, and motion transfer. As a challenging dataset, we hope Human-Art can provide insights for relevant research and open up new research questions.Comment: CVPR202