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Herkese Açık·7 üye
Isaiah Bennett
Isaiah Bennett

Producing Independent 2D Character Animation: M...

Computer-generated animation is known as three-dimensional (3D) animation. Creators design an object or character with an X, a Y and a Z axis. No pencil-to-paper drawings create the way computer-generated animation works. The object or character created will then be taken into a software. Key-framing and tweening are also carried out in computer-generated animation but so are many techniques unrelated to traditional animation. Animators can break physical laws by using mathematical algorithms to cheat mass, force and gravity rulings. Fundamentally, time scale and quality could be said to be a preferred way to produce animation as they are major aspects enhanced by using computer-generated animation. Another positive aspect of CGA is the fact one can create a flock of creatures to act independently when created as a group. An animal's fur can be programmed to wave in the wind and lie flat when it rains instead of separately programming each strand of hair.[60]

Producing Independent 2D Character Animation: M...


Animate, back when it was still called Flash, used to be the only practical way of creating animation for the web. Now, Animate's ease of use and low barrier to entry continue to make it a go-to for independent animators producing content for Youtube.

Radiosity is a surface-by-surface, camera-independent calculation technique which accounts for indirect illumination or bounced diffused light. The illumination on a surface comes not only directly from the light sources, but also from other surfaces reflecting light. Soft graduated shadows and color bleeding are among the major characteristics of renders created by the Radiosity technique. Being a viewpoint independent technique also results in an increase in calculations and therefore rendering time. But in terms of quality and the degree of photorealism, it would definitely be worth it.

Rigged puppets are one of the most prevalent representations to create 2D character animations. Creating these puppets requires partitioning characters into independently moving parts. In this work, we present a method to automatically identify such articulated parts from a small set of character poses shown in a sprite sheet, which is an illustration of the character that artists often draw before puppet creation. Our method is trained to infer articulated body parts, e.g. head, torso and limbs, that can be re-assembled to best reconstruct the given poses. Our results demonstrate significantly better performance than alternatives qualitatively and quantitatively.

We present RigNet, an end-to-end automated method for producing animation rigs from input character models. Given an input 3D model representing an articulated character, RigNet predicts a skeleton that matches the animator expectations in joint placement and topology. It also estimates surface skin weights based on the predicted skeleton. Our method is based on a deep architecture that directly operates on the mesh representation without making assumptions on shape class and structure. 041b061a72


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